[Image: source: /var/folders/q1/bsqp61pd41xbttpzqq6rpw_00000gn/T/TemporaryItems/NSIRD_screencaptureui_rutZ8f/Screenshot 2026-03-22 at 2.04.11 PM.png]
Two issues to fix:
Let me debug the follow-ups first.
.venv/bin/python -c "
import json
data = json.load(open('data/3b5b6e07-9143-4bbc-a305-962d2f60884c/session.json'))
last = data['messages'][-1]
print('follow_ups:', last.get('follow_ups', []))
print('charts:', len(last.get('charts', [])))
print('tool_calls:', len(last.get('tool_calls', [])))
print('sources:', len(last.get('sources', [])))
"The follow_ups list is empty in the stored message — the post_tool_hook isn't extracting them. The tool_calls (2) and sources (15) work because they use the new accumulator pattern. Charts and follow_ups still rely on the old hook extraction which apparently isn't receiving the results correctly.
The fix: move chart and follow_up extraction to the same accumulator pattern as tool_records and sources.
{
"file_path": "/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/tools.py",
"offset": 17,
"limit": 15
} 17→def create_review_tools_server(
18→ session_id: str,
19→ emit_fn: EmitFn,
20→ tool_records: list[dict] | None = None,
21→ cited_sources: list[dict] | None = None,
22→):
23→ """Create the MCP server with all review analysis tools.
24→
25→ Uses closure over session_id so tools access the right data.
26→ tool_records and cited_sources are accumulator lists populated by tools
27→ and read by agent.py to attach to the final ChatMessage.
28→ """
29→
30→ # Track which source IDs we've already collected
31→ _seen_source_ids: set[str] = set()def create_review_tools_server(
session_id: str,
emit_fn: EmitFn,
tool_records: list[dict] | None = None,
cited_sources: list[dict] | None = None,
):
"""Create the MCP server with all review analysis tools.
Uses closure over session_id so tools access the right data.
tool_records and cited_sources are accumulator lists populated by tools
and read by agent.py to attach to the final ChatMessage.
"""def create_review_tools_server(
session_id: str,
emit_fn: EmitFn,
tool_records: list[dict] | None = None,
cited_sources: list[dict] | None = None,
chart_accumulator: list[dict] | None = None,
follow_up_accumulator: list[str] | None = None,
):
"""Create the MCP server with all review analysis tools.
Uses closure over session_id so tools access the right data.
Accumulator lists are populated by tools and read by agent.py
to attach to the final ChatMessage.
"""The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/tools.py has been updated successfully.
{
"file_path": "/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/tools.py",
"offset": 198,
"limit": 40
} 198→ "reviews": results,
199→ "instruction": (
200→ "Analyse these reviews for sentiment and aspects. "
201→ "For each review, identify: (1) key aspects mentioned "
202→ "(e.g., 'battery life', 'customer service', 'price'), "
203→ "(2) sentiment per aspect (positive/negative/neutral/mixed), "
204→ "(3) overall sentiment. Then summarise the patterns across all reviews. "
205→ "Only report what the reviews actually say."
206→ ),
207→ }
208→ ),
209→ }
210→ ]
211→ }
212→
213→ # ── generate_chart ───────────────────────────────────────────────
214→
215→ @tool(
216→ name="generate_chart",
217→ description="Generate a Chart.js chart configuration that renders inline in the chat. Use for visualising rating distributions, sentiment breakdowns, trends over time, aspect comparisons, etc. The chart renders automatically — just return valid config.",
218→ input_schema={
219→ "type": "object",
220→ "properties": {
221→ "chart_type": {
222→ "type": "string",
223→ "enum": ["bar", "line", "pie", "doughnut"],
224→ "description": "The type of chart to generate.",
225→ },
226→ "title": {
227→ "type": "string",
228→ "description": "Chart title displayed above the visualisation.",
229→ },
230→ "labels": {
231→ "type": "array",
232→ "items": {"type": "string"},
233→ "description": "X-axis labels (categories, dates, etc.).",
234→ },
235→ "datasets": {
236→ "type": "array",
237→ "items": {{
"file_path": "/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/tools.py",
"offset": 248,
"limit": 35
} 248→ "description": "One or more data series.",
249→ },
250→ },
251→ "required": ["chart_type", "title", "labels", "datasets"],
252→ },
253→ )
254→ async def generate_chart_tool(args: dict[str, Any]) -> dict[str, Any]:
255→ chart_config = {
256→ "type": args["chart_type"],
257→ "title": args["title"],
258→ "data": {
259→ "labels": args["labels"],
260→ "datasets": args["datasets"],
261→ },
262→ }
263→
264→ await _emit_tool(
265→ "generate_chart",
266→ f"Generated chart: {args['title']}",
267→ {"chart_type": args["chart_type"], "title": args["title"]},
268→ {"labels_count": len(args["labels"]), "datasets_count": len(args["datasets"])},
269→ )
270→
271→ return {
272→ "content": [
273→ {
274→ "type": "text",
275→ "text": json.dumps(
276→ {
277→ "chart": chart_config,
278→ "instruction": "This chart will render automatically in the chat. Reference it in your text response.",
279→ }
280→ ),
281→ }
282→ ] async def generate_chart_tool(args: dict[str, Any]) -> dict[str, Any]:
chart_config = {
"type": args["chart_type"],
"title": args["title"],
"data": {
"labels": args["labels"],
"datasets": args["datasets"],
},
}
await _emit_tool( async def generate_chart_tool(args: dict[str, Any]) -> dict[str, Any]:
chart_config = {
"type": args["chart_type"],
"title": args["title"],
"data": {
"labels": args["labels"],
"datasets": args["datasets"],
},
}
if chart_accumulator is not None:
chart_accumulator.append(chart_config)
await _emit_tool(The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/tools.py has been updated successfully.
{
"file_path": "/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/tools.py",
"offset": 376,
"limit": 30
} 376→ result["percentage"] = round(count / len(all_reviews) * 100, 1)
377→
378→ elif operation == "summary_stats":
379→ ratings = [r["metadata"].get("rating") for r in all_reviews if r["metadata"].get("rating") is not None]
380→ result["total_reviews"] = len(all_reviews)
381→ result["total_with_ratings"] = len(ratings)
382→ if ratings:
383→ result["average_rating"] = round(sum(ratings) / len(ratings), 2)
384→ result["min_rating"] = min(ratings)
385→ result["max_rating"] = max(ratings)
386→ dates = [r["metadata"].get("date", "") for r in all_reviews if r["metadata"].get("date")]
387→ if dates:
388→ result["earliest_review"] = min(dates)
389→ result["latest_review"] = max(dates)
390→ avg_length = sum(len(r["text"]) for r in all_reviews) / len(all_reviews)
391→ result["average_review_length"] = round(avg_length)
392→
393→ await _emit_tool(
394→ "calculate_stats",
395→ f"Calculated stats: {operation}",
396→ {"operation": operation, "keyword": args.get("keyword", "")},
397→ {k: v for k, v in result.items() if k != "operation"},
398→ )
399→
400→ return {
401→ "content": [
402→ {
403→ "type": "text",
404→ "text": json.dumps(result),
405→ }{
"file_path": "/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/tools.py",
"offset": 421,
"limit": 30
} 421→ "minItems": 2,
422→ "maxItems": 4,
423→ },
424→ },
425→ "required": ["questions"],
426→ },
427→ )
428→ async def suggest_follow_ups_tool(args: dict[str, Any]) -> dict[str, Any]:
429→ questions = args["questions"]
430→
431→ await _emit_tool(
432→ "suggest_follow_ups",
433→ f"Suggested {len(questions)} follow-up questions",
434→ {"count": len(questions)},
435→ )
436→
437→ return {
438→ "content": [
439→ {
440→ "type": "text",
441→ "text": json.dumps(
442→ {
443→ "follow_ups": questions,
444→ "instruction": "These will appear as clickable buttons below your message. Do not repeat them in your text response.",
445→ }
446→ ),
447→ }
448→ ]
449→ }
450→ async def suggest_follow_ups_tool(args: dict[str, Any]) -> dict[str, Any]:
questions = args["questions"]
await _emit_tool( async def suggest_follow_ups_tool(args: dict[str, Any]) -> dict[str, Any]:
questions = args["questions"]
if follow_up_accumulator is not None:
follow_up_accumulator.extend(questions)
await _emit_tool(The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/tools.py has been updated successfully.
{
"file_path": "/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/agent.py",
"offset": 100,
"limit": 80
} 100→ system_prompt = build_system_prompt(summary)
101→
102→ # Build structured conversation context
103→ prompt = _build_conversation_context(conversation_history, user_message)
104→
105→ # Accumulators populated by tools via closure
106→ tool_records: list[dict] = []
107→ cited_sources: list[dict] = []
108→
109→ # Create per-request MCP server (closure over session_id)
110→ server = create_review_tools_server(
111→ session_id=session_id,
112→ emit_fn=emit_fn,
113→ tool_records=tool_records,
114→ cited_sources=cited_sources,
115→ )
116→
117→ # Track tool outputs for charts and follow-ups
118→ charts: list[dict[str, Any]] = []
119→ follow_ups: list[str] = []
120→
121→ async def post_tool_hook(input_data, tool_use_id, context):
122→ """Intercept tool results to extract charts and follow-ups."""
123→ try:
124→ result = context.get("result", {})
125→ content = result.get("content", [])
126→ for block in content:
127→ if block.get("type") == "text":
128→ data = json.loads(block["text"])
129→ if "chart" in data:
130→ charts.append(data["chart"])
131→ if "follow_ups" in data:
132→ follow_ups.extend(data["follow_ups"])
133→ except (json.JSONDecodeError, KeyError, TypeError):
134→ pass
135→ return {}
136→
137→ options = ClaudeAgentOptions(
138→ system_prompt=system_prompt,
139→ allowed_tools=["mcp__reviewlens__*"],
140→ permission_mode="bypassPermissions",
141→ max_turns=15,
142→ model=model,
143→ mcp_servers={"reviewlens": server},
144→ hooks={
145→ "PostToolUse": [HookMatcher(matcher=".*", hooks=[post_tool_hook])],
146→ },
147→ )
148→
149→ response_text = ""
150→
151→ try:
152→ async with ClaudeSDKClient(options=options) as client:
153→ await client.query(prompt)
154→ async for message in client.receive_response():
155→ if isinstance(message, AssistantMessage):
156→ for block in message.content:
157→ if isinstance(block, TextBlock) and block.text.strip():
158→ response_text += block.text.strip() + "\n"
159→ elif isinstance(message, ResultMessage):
160→ if message.result and not response_text.strip():
161→ response_text = message.result
162→
163→ except Exception as e:
164→ response_text = f"I encountered an error processing your question. Please try again.\n\nError: {str(e)}"
165→ await emit_fn(session_id, f"Agent error: {e}", "error")
166→
167→ return ChatMessage(
168→ role="assistant",
169→ content=response_text.strip(),
170→ charts=charts,
171→ follow_ups=follow_ups,
172→ tool_calls=[ToolCallRecord(**r) for r in tool_records],
173→ sources=cited_sources,
174→ )
175→ # Accumulators populated by tools via closure
tool_records: list[dict] = []
cited_sources: list[dict] = []
# Create per-request MCP server (closure over session_id)
server = create_review_tools_server(
session_id=session_id,
emit_fn=emit_fn,
tool_records=tool_records,
cited_sources=cited_sources,
)
# Track tool outputs for charts and follow-ups
charts: list[dict[str, Any]] = []
follow_ups: list[str] = []
async def post_tool_hook(input_data, tool_use_id, context):
"""Intercept tool results to extract charts and follow-ups."""
try:
result = context.get("result", {})
content = result.get("content", [])
for block in content:
if block.get("type") == "text":
data = json.loads(block["text"])
if "chart" in data:
charts.append(data["chart"])
if "follow_ups" in data:
follow_ups.extend(data["follow_ups"])
except (json.JSONDecodeError, KeyError, TypeError):
pass
return {}
options = ClaudeAgentOptions(
system_prompt=system_prompt,
allowed_tools=["mcp__reviewlens__*"],
permission_mode="bypassPermissions",
max_turns=15,
model=model,
mcp_servers={"reviewlens": server},
hooks={
"PostToolUse": [HookMatcher(matcher=".*", hooks=[post_tool_hook])],
},
) # Accumulators populated by tools via closure
tool_records: list[dict] = []
cited_sources: list[dict] = []
charts: list[dict[str, Any]] = []
follow_ups: list[str] = []
# Create per-request MCP server (closure over session_id)
server = create_review_tools_server(
session_id=session_id,
emit_fn=emit_fn,
tool_records=tool_records,
cited_sources=cited_sources,
chart_accumulator=charts,
follow_up_accumulator=follow_ups,
)
options = ClaudeAgentOptions(
system_prompt=system_prompt,
allowed_tools=["mcp__reviewlens__*"],
permission_mode="bypassPermissions",
max_turns=15,
model=model,
mcp_servers={"reviewlens": server},
)The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/agent.py has been updated successfully.
from claude_agent_sdk import (
AssistantMessage,
ClaudeAgentOptions,
ClaudeSDKClient,
HookMatcher,
ResultMessage,
TextBlock,
)from claude_agent_sdk import (
AssistantMessage,
ClaudeAgentOptions,
ClaudeSDKClient,
ResultMessage,
TextBlock,
)The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/agent.py has been updated successfully.
Now for the search depth issue — let me update the prompt to encourage multiple search passes.
{
"file_path": "/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/prompts.py",
"offset": 118,
"limit": 12
}118→ 119→1. **Ground every claim in data.** Every assertion must trace back to actual review search results or calculated statistics. If search returns nothing relevant, say so honestly rather than filling gaps. 120→2. **Cite specific reviews with source markers.** When quoting or paraphrasing a specific review, include its ID as a citation marker: `[source:review_id]`. For example: "One reviewer noted that the service was slow [source:review_42]." The system renders these as clickable citations showing the full review. Only cite review IDs that appeared in your search results. Each review in search results has an `id` field — use that. 121→3. **Be quantitative.** Counts, percentages, averages. Use calculate_stats for aggregations. "Many reviews mention X" is weak; "23% of negative reviews cite X" is strong. 122→4. **Charts serve the insight.** A chart adds value for distributions, trends over time, and comparisons. Don't chart a single number. Don't chart everything. 123→5. **Confidence awareness.** If search results are sparse or tangential, acknowledge the limitation. "Based on the 4 reviews that mention this topic..." is more honest than overstating a finding. 124→6. **Be concise.** Users are analysts who want insights, not essays. Lead with the finding, support with evidence, suggest next steps. 125→7. **Refuse gracefully.** If something is out of scope, decline and redirect to something you can answer from the data. 126→""" 127→
1. **Ground every claim in data.** Every assertion must trace back to actual review search results or calculated statistics. If search returns nothing relevant, say so honestly rather than filling gaps.
1. **Search thoroughly.** Don't rely on a single search query. Use multiple queries with different phrasings and angles to build a complete picture. For example, if asked about "toppings", also search "ingredients", "menu items", "pizza", "food quality" etc. Cast a wide net, then synthesise. The cost of an extra search is low; the cost of missing relevant reviews is high. 2. **Ground every claim in data.** Every assertion must trace back to actual review search results or calculated statistics. If search returns nothing relevant, say so honestly rather than filling gaps.
The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/prompts.py has been updated successfully.
{
"file_path": "/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/prompts.py",
"offset": 118,
"limit": 15
}118→ 119→1. **Search thoroughly.** Don't rely on a single search query. Use multiple queries with different phrasings and angles to build a complete picture. For example, if asked about "toppings", also search "ingredients", "menu items", "pizza", "food quality" etc. Cast a wide net, then synthesise. The cost of an extra search is low; the cost of missing relevant reviews is high. 120→2. **Ground every claim in data.** Every assertion must trace back to actual review search results or calculated statistics. If search returns nothing relevant, say so honestly rather than filling gaps. 121→2. **Cite specific reviews with source markers.** When quoting or paraphrasing a specific review, include its ID as a citation marker: `[source:review_id]`. For example: "One reviewer noted that the service was slow [source:review_42]." The system renders these as clickable citations showing the full review. Only cite review IDs that appeared in your search results. Each review in search results has an `id` field — use that. 122→3. **Be quantitative.** Counts, percentages, averages. Use calculate_stats for aggregations. "Many reviews mention X" is weak; "23% of negative reviews cite X" is strong. 123→4. **Charts serve the insight.** A chart adds value for distributions, trends over time, and comparisons. Don't chart a single number. Don't chart everything. 124→5. **Confidence awareness.** If search results are sparse or tangential, acknowledge the limitation. "Based on the 4 reviews that mention this topic..." is more honest than overstating a finding. 125→6. **Be concise.** Users are analysts who want insights, not essays. Lead with the finding, support with evidence, suggest next steps. 126→7. **Refuse gracefully.** If something is out of scope, decline and redirect to something you can answer from the data. 127→""" 128→
2. **Cite specific reviews with source markers.** When quoting or paraphrasing a specific review, include its ID as a citation marker: `[source:review_id]`. For example: "One reviewer noted that the service was slow [source:review_42]." The system renders these as clickable citations showing the full review. Only cite review IDs that appeared in your search results. Each review in search results has an `id` field — use that. 3. **Be quantitative.** Counts, percentages, averages. Use calculate_stats for aggregations. "Many reviews mention X" is weak; "23% of negative reviews cite X" is strong. 4. **Charts serve the insight.** A chart adds value for distributions, trends over time, and comparisons. Don't chart a single number. Don't chart everything. 5. **Confidence awareness.** If search results are sparse or tangential, acknowledge the limitation. "Based on the 4 reviews that mention this topic..." is more honest than overstating a finding. 6. **Be concise.** Users are analysts who want insights, not essays. Lead with the finding, support with evidence, suggest next steps. 7. **Refuse gracefully.** If something is out of scope, decline and redirect to something you can answer from the data.
3. **Cite specific reviews with source markers.** When quoting or paraphrasing a specific review, include its ID as a citation marker: `[source:review_id]`. For example: "One reviewer noted that the service was slow [source:review_42]." The system renders these as clickable citations showing the full review. Only cite review IDs that appeared in your search results. Each review in search results has an `id` field — use that. 4. **Be quantitative.** Counts, percentages, averages. Use calculate_stats for aggregations. "Many reviews mention X" is weak; "23% of negative reviews cite X" is strong. 5. **Charts serve the insight.** A chart adds value for distributions, trends over time, and comparisons. Don't chart a single number. Don't chart everything. 6. **Confidence awareness.** If search results are sparse or tangential, acknowledge the limitation. "Based on the 4 reviews that mention this topic..." is more honest than overstating a finding. 7. **Be concise.** Users are analysts who want insights, not essays. Lead with the finding, support with evidence, suggest next steps. 8. **Refuse gracefully.** If something is out of scope, decline and redirect to something you can answer from the data.
The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/prompts.py has been updated successfully.
python3 -c "
import ast
for f in ['app/tools.py', 'app/agent.py', 'app/prompts.py']:
ast.parse(open(f).read())
print(f'{f}: OK')
"lsof -ti :8000 2>/dev/null | xargs kill 2>/dev/null; sleep 1 && .venv/bin/uvicorn app.main:app --reload --reload-exclude '.venv' --reload-exclude 'data'
Command running in background with ID: bloazztof. Output is being written to: /private/tmp/claude-501/-Users-shaynelarocque-Documents-GitHub-futuresight/5c8423a6-db28-4eb0-98eb-ccb8759929ff/tasks/bloazztof.output
Server's up. Two fixes applied:
Follow-ups + charts now use the accumulator pattern — moved from the broken post_tool_hook (which wasn't receiving results correctly) to direct accumulation in the tool closures, same pattern as tool_records and cited_sources. The hook has been removed entirely.
Search depth — added guideline #1: "Search thoroughly. Don't rely on a single search query. Use multiple queries with different phrasings and angles." This should push the agent to cast a wider net before synthesising.
Read the output file to retrieve the result: /private/tmp/claude-501/-Users-shaynelarocque-Documents-GitHub-futuresight/5c8423a6-db28-4eb0-98eb-ccb8759929ff/tasks/b5h6z1g7d.output
cd /Users/shaynelarocque/Documents/GitHub/futuresight && git status
cd /Users/shaynelarocque/Documents/GitHub/futuresight && git diff --stat
<tool_use_error>Cancelled: parallel tool call Bash(cd /Users/shaynelarocque/Documents/GitHu…) errored</tool_use_error>
cd /Users/shaynelarocque/Documents/GitHub/futuresight && git log --oneline -5
<tool_use_error>Cancelled: parallel tool call Bash(cd /Users/shaynelarocque/Documents/GitHu…) errored</tool_use_error>
On branch main Your branch is up to date with 'origin/main'. Changes not staged for commit: modified: app/agent.py modified: app/main.py modified: app/models.py modified: app/prompts.py modified: app/store.py modified: app/tools.py modified: static/app.js modified: static/style.css modified: templates/app.html Untracked files: app/knowledge.py knowledge/ no changes added to commit (use "git add" and/or "git commit -a")
app/agent.py | 114 +++++++++++----- app/main.py | 158 +++++++++++++++++++++- app/models.py | 12 ++ app/prompts.py | 99 +++++++++++--- app/store.py | 16 +++ app/tools.py | 381 ++++++++++++++++++++++++++++++++++++++++++++++++++--- static/app.js | 39 +++++- static/style.css | 159 ++++++++++++++++++++++ templates/app.html | 10 +- 9 files changed, 896 insertions(+), 92 deletions(-)
git add app/knowledge.py knowledge/review-analysis-framework.md knowledge/analysis-patterns.md knowledge/report-structure.md app/agent.py app/main.py app/models.py app/prompts.py app/store.py app/tools.py static/app.js static/style.css templates/app.html
git commit -m "$(cat <<'EOF' Rearchitect to sandbox agent pattern with UI inspectability Sandbox pattern: Add /knowledge directory with ORM domain reference files, knowledge.py loader module, and restructure system prompt into four quadrants (Knowledge, Tools, Goal, Guidelines) while preserving scope guard. New tools: list_knowledge_files, read_knowledge_file, save_to_report, get_report, check_scope — alongside existing 5 tools. Report system: Session model gains report_findings dict, store.py gets append_finding/get_findings helpers, report generation endpoint added. Agent improvements: Structured conversation context with topic summaries for older messages instead of flat string truncation. Multiple search queries encouraged via prompt guideline. UI inspectability: Tool activity accordion per message (live during SSE, persisted on refresh), chart data tables with View Data toggle, inline [source:review_id] citations rendered as hoverable popovers. Bug fixes: Page refresh now renders markdown/charts/follow-ups via Jinja2 render_message filter. Charts and follow-ups use accumulator pattern instead of broken post-tool hooks. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com> EOF )"
ok
13 files changed, 1180 insertions(+), 92 deletions(-)
markupsafe is imported directly in app/main.py (line 7: from markupsafe import Markup) but isn't listed in requirements.txt. Add it explicitly — it ships as a Jinja2 dependency but direct imports should be declared.
When a dataset finishes ingesting (CSV upload or sample load), instead of showing a static welcome message, automatically trigger an agent analysis pass. The user's first experience should be watching the agent work through the data and produce a substantive initial briefing — before they type anything.
The user sees:
1. The chat page loads
2. A thinking indicator appears immediately with the tool accordion showing live tool calls (search_reviews, calculate_stats, analyze_sentiment, generate_chart, etc.)
3. 15-30 seconds later, a full opening briefing appears: rating distribution chart, top praised aspects with cited quotes, top complaints with cited quotes, risk signals, and contextual follow-up buttons
4. Key findings are saved to the report automatically via save_to_report
This mirrors the pattern from my BriefBot project where an agent autonomously produces a research brief when given a dataset — but here it's an analysis brief, not a research brief.
1. Add a synthetic first message after ingestion.
In app/main.py, after ingestion completes and status is set to "ready" (in both the upload_csv and load_sample endpoints), create and save a synthetic assistant-triggering message. Don't show it as a user bubble — the user should just see the agent's analysis appear as the first message.
The approach: save a system-initiated ChatMessage with role="user" and a specific content like "[initial_analysis]" (or similar marker), then trigger _run_agent_and_respond with the actual detailed prompt below. In the template, don't render user messages that start with [initial_analysis] — or better yet, use a flag/field on ChatMessage to mark it as system-initiated so it doesn't render as a user bubble.
The prompt to send to the agent (this is the synthetic "user message" the agent receives):
Analyse this dataset and provide an initial intelligence briefing. This is the analyst's first look at the data — make it count.
Cover these areas:
1. Dataset overview with a rating distribution chart
2. Top 3 most praised aspects with specific review citations
3. Top 3 complaints or pain points with specific review citations
4. Any notable risk signals, emerging trends, or inconsistencies worth flagging
5. A brief overall sentiment assessment
Use multiple search queries with different angles to be thorough. Generate at least one chart. Save the most significant findings to the report. End with follow-up suggestions that drill into the most interesting patterns you found.
Consult the knowledge base (list_knowledge_files → read_knowledge_file) if you need analytical frameworks for your analysis.
2. Trigger the analysis automatically.
After the redirect to /chat/{session_id}, the agent analysis should already be running. The cleanest way:
upload_csv and load_sample, after setting status to "ready", save the synthetic user message and kick off _run_agent_and_respond as an asyncio.create_task — same pattern as the existing send_message endpoint.The client-side JS already handles this flow — listenForResponse() connects to the SSE stream and shows thinking indicators. You just need to make sure the stream is active when the page loads. One approach: on the chat page, if there are no messages yet (fresh session), auto-connect to the SSE stream immediately on page load rather than waiting for a user send.
3. Update the template.
In templates/app.html, the {% if not messages %} block currently shows the static welcome. Change this:
The synthetic user message (the [initial_analysis] trigger) should NOT render as a user bubble. Either filter it out in the template, or mark it with a field on ChatMessage (e.g., system_initiated: bool = False) and skip rendering for system-initiated messages.
4. Consider the auto-analysis status.
Add a brief transitional state. When the user lands on the chat page and the auto-analysis is running, they should see something better than just the thinking dots. The tool accordion will populate as the agent works, which is good — but a small header text like "Generating initial analysis..." above the thinking indicator would make it clear what's happening.
app/agent.py) — the existing handle_message function handles this finemax_turns=15 in agent.py should be sufficient for the initial briefing (typically 8-12 tool calls), but monitor this. If the agent is getting cut off, consider bumping to 20 for the initial analysis pass specifically.{
"subject": "Add markupsafe to requirements.txt",
"activeForm": "Fixing requirements"
}{
"subject": "Add system_initiated field to ChatMessage",
"activeForm": "Updating model"
}{
"subject": "Auto-analysis trigger in upload_csv and load_sample",
"activeForm": "Adding auto-analysis trigger"
}{
"subject": "Update template for auto-analysis state",
"activeForm": "Updating template"
}{
"subject": "Update app.js for auto-SSE on fresh sessions",
"activeForm": "Updating app.js"
}{
"subject": "Update _render_message to skip system_initiated messages",
"activeForm": "Updating renderer"
}{
"file_path": "/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/requirements.txt"
} 1→fastapi>=0.115.0
2→uvicorn[standard]
3→sse-starlette
4→claude-agent-sdk
5→anthropic
6→chromadb
7→httpx
8→jinja2
9→python-multipart
10→python-dotenv
11→markdown
12→{
"file_path": "/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/models.py"
} 1→"""Pydantic models for ReviewLens."""
2→
3→from __future__ import annotations
4→
5→from datetime import datetime
6→from enum import Enum
7→from typing import Any
8→
9→from pydantic import BaseModel, Field
10→
11→
12→class Review(BaseModel):
13→ """A single review record."""
14→
15→ id: str = ""
16→ text: str
17→ rating: float | None = None
18→ date: datetime | None = None
19→ author: str = ""
20→ platform: str = ""
21→ metadata: dict[str, Any] = Field(default_factory=dict)
22→
23→
24→class IngestionSummary(BaseModel):
25→ """Summary shown after ingestion."""
26→
27→ total_reviews: int = 0
28→ date_range: str = ""
29→ rating_distribution: dict[str, int] = Field(default_factory=dict)
30→ average_rating: float | None = None
31→ platform: str = ""
32→ product_name: str = ""
33→ source_type: str = "" # "csv" or "url"
34→
35→
36→class ToolCallRecord(BaseModel):
37→ """A single tool invocation record for the activity accordion."""
38→
39→ tool_name: str
40→ summary: str
41→ inputs: dict[str, Any] = Field(default_factory=dict)
42→ output_summary: dict[str, Any] = Field(default_factory=dict)
43→
44→
45→class ChatMessage(BaseModel):
46→ """A single chat message."""
47→
48→ role: str # "user" or "assistant"
49→ content: str
50→ charts: list[dict[str, Any]] = Field(default_factory=list)
51→ follow_ups: list[str] = Field(default_factory=list)
52→ tool_calls: list[ToolCallRecord] = Field(default_factory=list)
53→ sources: list[dict[str, Any]] = Field(default_factory=list)
54→ timestamp: datetime = Field(default_factory=datetime.utcnow)
55→
56→
57→class Session(BaseModel):
58→ """A review analysis session."""
59→
60→ session_id: str
61→ product_name: str = ""
62→ platform: str = ""
63→ summary: IngestionSummary = Field(default_factory=IngestionSummary)
64→ messages: list[ChatMessage] = Field(default_factory=list)
65→ report_findings: dict[str, list[str]] = Field(default_factory=dict)
66→ status: str = "pending" # pending, ready, error
67→ created_at: datetime = Field(default_factory=datetime.utcnow)
68→markdown
markdown markupsafe
The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/requirements.txt has been updated successfully.
role: str # "user" or "assistant"
content: str
charts: list[dict[str, Any]] = Field(default_factory=list) role: str # "user" or "assistant"
content: str
system_initiated: bool = False
charts: list[dict[str, Any]] = Field(default_factory=list)The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/models.py has been updated successfully.
Now the main changes — main.py (auto-analysis trigger + renderer skip), template, and app.js. Let me read main.py for the upload/sample endpoints.
{
"file_path": "/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/main.py",
"offset": 112,
"limit": 115
} 112→
113→
114→@app.get("/chat/{session_id}", response_class=HTMLResponse)
115→async def chat_page(request: Request, session_id: str):
116→ session = store.load_session(session_id)
117→ if not session:
118→ return HTMLResponse("<h1>Session not found</h1>", status_code=404)
119→ return templates.TemplateResponse("app.html", _shell_context(request, session))
120→
121→
122→# ── CSV Upload ───────────────────────────────────────────────────────
123→
124→@app.post("/upload")
125→async def upload_csv(
126→ request: Request,
127→ file: UploadFile = File(...),
128→ product_name: str = Form(""),
129→ platform: str = Form(""),
130→):
131→ session_id = str(uuid.uuid4())
132→
133→ content = await file.read()
134→ try:
135→ reviews = await parse_csv(content, platform=platform, product_name=product_name)
136→ except Exception as e:
137→ return templates.TemplateResponse(
138→ "partials/error.html",
139→ {"request": request, "error": f"Failed to parse CSV: {e}"},
140→ status_code=400,
141→ )
142→
143→ if not reviews:
144→ return templates.TemplateResponse(
145→ "partials/error.html",
146→ {"request": request, "error": "No reviews found in the CSV. Make sure it has a text/review column."},
147→ status_code=400,
148→ )
149→
150→ summary = build_summary(reviews, source_type="csv", product_name=product_name, platform=platform)
151→
152→ session = Session(
153→ session_id=session_id,
154→ product_name=summary.product_name,
155→ platform=summary.platform,
156→ summary=summary,
157→ status="indexing",
158→ )
159→ store.save_session(session)
160→ store.save_reviews_json(session_id, [r.model_dump(mode="json") for r in reviews])
161→
162→ indexed = vectordb.index_reviews(session_id, reviews)
163→ summary.total_reviews = indexed
164→ store.update_summary(session_id, summary)
165→ store.set_status(session_id, "ready")
166→
167→ return HTMLResponse(
168→ status_code=200,
169→ content=f'<script>window.location.href="/chat/{session_id}";</script>',
170→ headers={"HX-Redirect": f"/chat/{session_id}"},
171→ )
172→
173→
174→# ── Sample Data ──────────────────────────────────────────────────────
175→
176→@app.post("/sample")
177→async def load_sample(
178→ request: Request,
179→ filename: str = Form(...),
180→):
181→ # Sanitise: only allow filenames that exist in sample-data/
182→ path = SAMPLE_DIR / filename
183→ if not path.exists() or not path.suffix == ".csv" or ".." in filename:
184→ return templates.TemplateResponse(
185→ "partials/error.html",
186→ {"request": request, "error": "Sample file not found."},
187→ status_code=400,
188→ )
189→
190→ content = path.read_bytes()
191→ # Derive product name and platform from the filename
192→ stem = path.stem.replace("_", " ").replace("-", " ")
193→ product_name = stem.title()
194→ platform = ""
195→ for plat in ("Amazon", "Google Maps", "G2", "Capterra", "Trustpilot", "Yelp"):
196→ if plat.lower().replace(" ", "_") in path.stem.lower() or plat.lower().replace(" ", "") in path.stem.lower():
197→ platform = plat
198→ break
199→
200→ reviews = await parse_csv(content, platform=platform, product_name=product_name)
201→
202→ if not reviews:
203→ return templates.TemplateResponse(
204→ "partials/error.html",
205→ {"request": request, "error": "Could not parse reviews from this sample file."},
206→ status_code=400,
207→ )
208→
209→ session_id = str(uuid.uuid4())
210→ summary = build_summary(reviews, source_type="csv", product_name=product_name, platform=platform)
211→
212→ session = Session(
213→ session_id=session_id,
214→ product_name=summary.product_name,
215→ platform=summary.platform,
216→ summary=summary,
217→ status="indexing",
218→ )
219→ store.save_session(session)
220→ store.save_reviews_json(session_id, [r.model_dump(mode="json") for r in reviews])
221→
222→ indexed = vectordb.index_reviews(session_id, reviews)
223→ summary.total_reviews = indexed
224→ store.update_summary(session_id, summary)
225→ store.set_status(session_id, "ready")
226→Now I'll add the auto-analysis prompt constant and a helper function, then modify both endpoints. Let me also read the _render_message function to update it.
{
"file_path": "/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/main.py",
"offset": 440,
"limit": 20
} 440→ """Replace [source:review_id] markers with clickable citation popovers."""
441→ if not sources:
442→ return html
443→ source_map = {s["id"]: s for s in sources}
444→
445→ def _replace(match):
446→ review_id = match.group(1)
447→ source = source_map.get(review_id)
448→ if not source:
449→ return match.group(0)
450→ text = html_module.escape(source.get("text", "")[:300])
451→ rating = source.get("rating", "")
452→ date = source.get("date", "")
453→ author = html_module.escape(source.get("author", "") or "Anonymous")
454→ meta_parts = [author]
455→ if rating:
456→ meta_parts.append(f"{rating}/5")
457→ if date:
458→ meta_parts.append(str(date)[:10])
459→ meta = " · ".join(meta_parts){
"file_path": "/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/main.py",
"offset": 466,
"limit": 10
}466→ f'</span></span>' 467→ ) 468→ 469→ return re.sub(r'\[source:([^\]]+)\]', _replace, html) 470→ 471→ 472→def _render_message(msg: ChatMessage) -> str: 473→ role_class = "user-message" if msg.role == "user" else "assistant-message" 474→ escaped = html_module.escape(msg.content) 475→
Now let me make all the changes. First, add the auto-analysis constant and helper, modify both endpoints, and update the renderer.
# ── Health check ─────────────────────────────────────────────────────
# ── Auto-analysis prompt ─────────────────────────────────────────────
_INITIAL_ANALYSIS_PROMPT = (
"Analyse this dataset and provide an initial intelligence briefing. "
"This is the analyst's first look at the data — make it count.\n\n"
"Cover these areas:\n"
"1. Dataset overview with a rating distribution chart\n"
"2. Top 3 most praised aspects with specific review citations\n"
"3. Top 3 complaints or pain points with specific review citations\n"
"4. Any notable risk signals, emerging trends, or inconsistencies worth flagging\n"
"5. A brief overall sentiment assessment\n\n"
"Use multiple search queries with different angles to be thorough. "
"Generate at least one chart. Save the most significant findings to the report. "
"End with follow-up suggestions that drill into the most interesting patterns you found.\n\n"
"Consult the knowledge base (list_knowledge_files → read_knowledge_file) "
"if you need analytical frameworks for your analysis."
)
def _trigger_auto_analysis(session_id: str, session: Session) -> None:
"""Save a synthetic message and kick off the initial analysis agent run."""
trigger_msg = ChatMessage(
role="user",
content="[initial_analysis]",
system_initiated=True,
)
store.append_message(session_id, trigger_msg)
_get_response_event(session_id).clear()
asyncio.create_task(
_run_agent_and_respond(session_id, _INITIAL_ANALYSIS_PROMPT, session)
)
# ── Health check ─────────────────────────────────────────────────────The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/main.py has been updated successfully.
indexed = vectordb.index_reviews(session_id, reviews)
summary.total_reviews = indexed
store.update_summary(session_id, summary)
store.set_status(session_id, "ready")
return HTMLResponse(
status_code=200,
content=f'<script>window.location.href="/chat/{session_id}";</script>',
headers={"HX-Redirect": f"/chat/{session_id}"},
)
# ── Sample Data ────────────────────────────────────────────────────── indexed = vectordb.index_reviews(session_id, reviews)
summary.total_reviews = indexed
store.update_summary(session_id, summary)
store.set_status(session_id, "ready")
# Kick off auto-analysis
session = store.load_session(session_id)
if session:
_trigger_auto_analysis(session_id, session)
return HTMLResponse(
status_code=200,
content=f'<script>window.location.href="/chat/{session_id}";</script>',
headers={"HX-Redirect": f"/chat/{session_id}"},
)
# ── Sample Data ──────────────────────────────────────────────────────The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/main.py has been updated successfully.
{
"file_path": "/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/main.py",
"offset": 248,
"limit": 20
} 248→ summary = build_summary(reviews, source_type="csv", product_name=product_name, platform=platform)
249→
250→ session = Session(
251→ session_id=session_id,
252→ product_name=summary.product_name,
253→ platform=summary.platform,
254→ summary=summary,
255→ status="indexing",
256→ )
257→ store.save_session(session)
258→ store.save_reviews_json(session_id, [r.model_dump(mode="json") for r in reviews])
259→
260→ indexed = vectordb.index_reviews(session_id, reviews)
261→ summary.total_reviews = indexed
262→ store.update_summary(session_id, summary)
263→ store.set_status(session_id, "ready")
264→
265→ return HTMLResponse(
266→ status_code=200,
267→ content=f'<script>window.location.href="/chat/{session_id}";</script>', indexed = vectordb.index_reviews(session_id, reviews)
summary.total_reviews = indexed
store.update_summary(session_id, summary)
store.set_status(session_id, "ready")
return HTMLResponse(
status_code=200,
content=f'<script>window.location.href="/chat/{session_id}";</script>',
headers={"HX-Redirect": f"/chat/{session_id}"},
)
# ── URL Scrape ─────────────────────────────────────────────────────── indexed = vectordb.index_reviews(session_id, reviews)
summary.total_reviews = indexed
store.update_summary(session_id, summary)
store.set_status(session_id, "ready")
# Kick off auto-analysis
session = store.load_session(session_id)
if session:
_trigger_auto_analysis(session_id, session)
return HTMLResponse(
status_code=200,
content=f'<script>window.location.href="/chat/{session_id}";</script>',
headers={"HX-Redirect": f"/chat/{session_id}"},
)
# ── URL Scrape ───────────────────────────────────────────────────────The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/main.py has been updated successfully.
def _render_message(msg: ChatMessage) -> str:
role_class = "user-message" if msg.role == "user" else "assistant-message"
escaped = html_module.escape(msg.content)def _render_message(msg: ChatMessage) -> str:
# Skip system-initiated trigger messages (auto-analysis)
if msg.system_initiated and msg.role == "user":
return ""
role_class = "user-message" if msg.role == "user" else "assistant-message"
escaped = html_module.escape(msg.content)The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/main.py has been updated successfully.
Now update the template and app.js. The template needs to show the thinking indicator on fresh sessions and auto-connect SSE. Let me also add a auto_analysis context flag.
{
"file_path": "/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/main.py",
"offset": 118,
"limit": 12
} 118→ files = []
119→ for f in sorted(SAMPLE_DIR.glob("*.csv")):
120→ label = f.stem.replace("_", " ").replace("-", " ").title()
121→ files.append({"filename": f.name, "label": label})
122→ return files
123→
124→
125→# ── App shell (home + chat share the same layout) ───────────────────
126→
127→def _shell_context(request: Request, session=None):
128→ """Build template context for the app shell."""
129→ sessions = store.list_sessions(){
"file_path": "/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/main.py",
"offset": 127,
"limit": 20
} 127→def _shell_context(request: Request, session=None):
128→ """Build template context for the app shell."""
129→ sessions = store.list_sessions()
130→ ctx = {
131→ "request": request,
132→ "sessions": sessions,
133→ "session": session,
134→ "summary": session.summary if session else None,
135→ "messages": session.messages if session else [],
136→ "active_id": session.session_id if session else None,
137→ "sample_files": _list_sample_files(),
138→ }
139→ return ctx
140→
141→
142→@app.get("/", response_class=HTMLResponse)
143→async def index(request: Request):
144→ return templates.TemplateResponse("app.html", _shell_context(request))
145→
146→I need to add an auto_analysis flag to the context so the template knows to show the thinking indicator.
def _shell_context(request: Request, session=None):
"""Build template context for the app shell."""
sessions = store.list_sessions()
ctx = {
"request": request,
"sessions": sessions,
"session": session,
"summary": session.summary if session else None,
"messages": session.messages if session else [],
"active_id": session.session_id if session else None,
"sample_files": _list_sample_files(),
}
return ctxdef _shell_context(request: Request, session=None):
"""Build template context for the app shell."""
sessions = store.list_sessions()
# Filter out system-initiated messages for display
messages = session.messages if session else []
visible_messages = [m for m in messages if not (m.system_initiated and m.role == "user")]
# Detect if auto-analysis is in progress (has trigger message but no assistant response yet)
auto_analysis = False
if session and session.status == "ready":
has_trigger = any(m.system_initiated for m in messages)
has_response = any(m.role == "assistant" for m in messages)
auto_analysis = has_trigger and not has_response
ctx = {
"request": request,
"sessions": sessions,
"session": session,
"summary": session.summary if session else None,
"messages": visible_messages,
"active_id": session.session_id if session else None,
"sample_files": _list_sample_files(),
"auto_analysis": auto_analysis,
}
return ctxThe file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/app/main.py has been updated successfully.
{
"file_path": "/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/templates/app.html",
"offset": 154,
"limit": 35
} 154→ {% elif session %}
155→ <!-- Chat view -->
156→ <div class="chat-messages" id="chat-messages">
157→ {% for msg in messages %}
158→ {{ msg|render_message }}
159→ {% endfor %}
160→
161→ {% if not messages %}
162→ <div class="message assistant-message welcome">
163→ <div class="message-content">
164→ <p>I have <strong>{{ summary.total_reviews }} reviews</strong> loaded for <strong>{{ summary.product_name }}</strong> from <strong>{{ summary.platform }}</strong>{% if summary.date_range %}, spanning <strong>{{ summary.date_range }}</strong>{% endif %}.</p>
165→ <p>I can only answer questions grounded in this dataset. What would you like to explore?</p>
166→ </div>
167→ <div class="follow-ups">
168→ <button class="follow-up-btn" onclick="sendFollowUp(this)" data-question="What's the overall sentiment breakdown?">Sentiment breakdown</button>
169→ <button class="follow-up-btn" onclick="sendFollowUp(this)" data-question="What are the most common complaints?">Top complaints</button>
170→ <button class="follow-up-btn" onclick="sendFollowUp(this)" data-question="Show me the rating distribution over time">Ratings over time</button>
171→ <button class="follow-up-btn" onclick="sendFollowUp(this)" data-question="What do people praise the most?">Most praised aspects</button>
172→ </div>
173→ </div>
174→ {% endif %}
175→ </div>
176→
177→ <div class="chat-input-area">
178→ <form id="chat-form" onsubmit="sendMessage(event); return false;">
179→ <div class="input-row">
180→ <textarea id="chat-input" name="message" placeholder="Ask about the reviews..." rows="1" autofocus></textarea>
181→ <button type="submit" class="btn-send" id="send-btn">
182→ <svg width="18" height="18" viewBox="0 0 24 24" fill="none" stroke="currentColor" stroke-width="2" stroke-linecap="round" stroke-linejoin="round">
183→ <line x1="5" y1="12" x2="19" y2="12"/><polyline points="12 5 19 12 12 19"/>
184→ </svg>
185→ </button>
186→ </div>
187→ </form>
188→ <p class="input-hint">Analyses only the uploaded dataset. No external knowledge.</p> {% for msg in messages %}
{{ msg|render_message }}
{% endfor %}
{% if not messages %}
<div class="message assistant-message welcome">
<div class="message-content">
<p>I have <strong>{{ summary.total_reviews }} reviews</strong> loaded for <strong>{{ summary.product_name }}</strong> from <strong>{{ summary.platform }}</strong>{% if summary.date_range %}, spanning <strong>{{ summary.date_range }}</strong>{% endif %}.</p>
<p>I can only answer questions grounded in this dataset. What would you like to explore?</p>
</div>
<div class="follow-ups">
<button class="follow-up-btn" onclick="sendFollowUp(this)" data-question="What's the overall sentiment breakdown?">Sentiment breakdown</button>
<button class="follow-up-btn" onclick="sendFollowUp(this)" data-question="What are the most common complaints?">Top complaints</button>
<button class="follow-up-btn" onclick="sendFollowUp(this)" data-question="Show me the rating distribution over time">Ratings over time</button>
<button class="follow-up-btn" onclick="sendFollowUp(this)" data-question="What do people praise the most?">Most praised aspects</button>
</div>
</div>
{% endif %} {% for msg in messages %}
{{ msg|render_message }}
{% endfor %}
{% if auto_analysis %}
<!-- Auto-analysis in progress -->
<div id="thinking-indicator" class="message assistant-message thinking">
<div class="message-content">
<p class="auto-analysis-label">Generating initial analysis...</p>
<div class="thinking-dots"><span></span><span></span><span></span></div>
</div>
</div>
{% elif not messages %}
<div class="message assistant-message welcome">
<div class="message-content">
<p>I have <strong>{{ summary.total_reviews }} reviews</strong> loaded for <strong>{{ summary.product_name }}</strong> from <strong>{{ summary.platform }}</strong>{% if summary.date_range %}, spanning <strong>{{ summary.date_range }}</strong>{% endif %}.</p>
<p>I can only answer questions grounded in this dataset. What would you like to explore?</p>
</div>
<div class="follow-ups">
<button class="follow-up-btn" onclick="sendFollowUp(this)" data-question="What's the overall sentiment breakdown?">Sentiment breakdown</button>
<button class="follow-up-btn" onclick="sendFollowUp(this)" data-question="What are the most common complaints?">Top complaints</button>
<button class="follow-up-btn" onclick="sendFollowUp(this)" data-question="Show me the rating distribution over time">Ratings over time</button>
<button class="follow-up-btn" onclick="sendFollowUp(this)" data-question="What do people praise the most?">Most praised aspects</button>
</div>
</div>
{% endif %}The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/templates/app.html has been updated successfully.
{
"file_path": "/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/templates/app.html",
"offset": 360,
"limit": 15
} 360→ </button>
361→
362→ <div id="sample-result"></div>
363→ </form>
364→ </div>
365→ </div>
366→
367→ <script>
368→ var SESSION_ID = "{{ active_id or '' }}";
369→ </script>
370→ <script src="/static/app.js"></script>
371→</body>
372→</html>
373→ <script>
var SESSION_ID = "{{ active_id or '' }}";
</script> <script>
var SESSION_ID = "{{ active_id or '' }}";
var AUTO_ANALYSIS = {{ 'true' if auto_analysis else 'false' }};
</script>The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/templates/app.html has been updated successfully.
Now add auto-SSE connection in app.js. I'll add it near the bottom, after the scrollToBottom() and runChartScripts() calls.
{
"file_path": "/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/static/app.js",
"offset": 490,
"limit": 20
} 490→function runChartScripts() {
491→ if (!chatMessages) return;
492→ var scripts = chatMessages.querySelectorAll("script");
493→ scripts.forEach(function (script) {
494→ if (script.textContent.indexOf("renderChart") !== -1 && !script.dataset.executed) {
495→ script.dataset.executed = "true";
496→ eval(script.textContent);
497→ }
498→ });
499→}
500→
501→function toggleChartData(id) {
502→ var el = document.getElementById(id);
503→ if (!el) return;
504→ var btn = el.previousElementSibling;
505→ if (el.style.display === "none") {
506→ el.style.display = "block";
507→ if (btn && btn.classList.contains("chart-data-toggle")) btn.textContent = "Hide data";
508→ } else {
509→ el.style.display = "none";{
"file_path": "/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/static/app.js",
"offset": 510,
"limit": 35
} 510→ if (btn && btn.classList.contains("chart-data-toggle")) btn.textContent = "View data";
511→ }
512→}
513→
514→// ── Utilities ────────────────────────────────────────────────────────
515→
516→function escapeHtml(str) {
517→ var div = document.createElement("div");
518→ div.appendChild(document.createTextNode(str));
519→ return div.innerHTML;
520→}
521→
522→if (chatMessages) {
523→ var observer = new MutationObserver(scrollToBottom);
524→ observer.observe(chatMessages, { childList: true, subtree: true });
525→}
526→
527→scrollToBottom();
528→runChartScripts();
529→
530→// ── Scraping status poll ────────────────────────────────────────────
531→
532→(function () {
533→ var scrapingView = document.getElementById("scraping-view");
534→ if (!scrapingView || !SESSION_ID) return;
535→
536→ var stepNav = document.getElementById("step-navigating");
537→ var stepIdx = document.getElementById("step-indexing");
538→
539→ setTimeout(function () {
540→ if (stepNav) stepNav.classList.add("active");
541→ }, 3000);
542→
543→ var poller = setInterval(function () {
544→ fetch("/api/status/" + SESSION_ID)scrollToBottom(); runChartScripts(); // ── Scraping status poll ────────────────────────────────────────────
scrollToBottom();
runChartScripts();
// ── Auto-analysis SSE connection ────────────────────────────────────
if (typeof AUTO_ANALYSIS !== "undefined" && AUTO_ANALYSIS && SESSION_ID) {
_log.info("Auto-analysis in progress — connecting to SSE stream");
_sending = true;
if (chatInput) chatInput.disabled = true;
if (sendBtn) sendBtn.disabled = true;
listenForResponse();
}
// ── Scraping status poll ────────────────────────────────────────────The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/static/app.js has been updated successfully.
{
"file_path": "/Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/static/style.css",
"offset": 700,
"limit": 10
} 700→/* ── Thinking ──────────────────────────────────────────────────── */
701→
702→.thinking .message-content {
703→ display: flex;
704→ flex-direction: column;
705→ gap: 0.5rem;
706→ min-width: 100px;
707→}
708→
709→.thinking-dots {/* ── Thinking ──────────────────────────────────────────────────── */
.thinking .message-content {
display: flex;
flex-direction: column;
gap: 0.5rem;
min-width: 100px;
}/* ── Thinking ──────────────────────────────────────────────────── */
.auto-analysis-label {
font-size: 0.78rem;
color: var(--accent);
font-weight: 500;
margin: 0;
}
.thinking .message-content {
display: flex;
flex-direction: column;
gap: 0.5rem;
min-width: 100px;
}The file /Users/shaynelarocque/Documents/GitHub/futuresight/reviewlens/static/style.css has been updated successfully.
python3 -c "
import ast
for f in ['app/main.py', 'app/models.py']:
ast.parse(open(f).read())
print(f'{f}: OK')
"lsof -ti :8000 2>/dev/null | xargs kill 2>/dev/null; sleep 1 && .venv/bin/uvicorn app.main:app --reload --reload-exclude '.venv' --reload-exclude 'data'
Command running in background with ID: bi72w9nw0. Output is being written to: /private/tmp/claude-501/-Users-shaynelarocque-Documents-GitHub-futuresight/5c8423a6-db28-4eb0-98eb-ccb8759929ff/tasks/bi72w9nw0.output
Server's up. Here's what was done:
Bug fix: Added markupsafe to requirements.txt.
Auto-analysis feature:
ChatMessage.system_initiated — new boolean field to mark synthetic trigger messages_trigger_auto_analysis() — helper that saves the trigger message and kicks off the agent via asyncio.create_taskupload_csv and load_sample — call _trigger_auto_analysis() after setting status to "ready"_render_message — skips rendering system-initiated user messages (no bubble)_shell_context — filters out system-initiated messages from display, sets auto_analysis=True when the trigger exists but no response yetauto_analysis is trueAUTO_ANALYSIS is true, so the tool accordion builds liveThe flow: Upload CSV → ingestion → auto-analysis kicks off → redirect to chat → page loads with thinking indicator → SSE connects → tool accordion shows live progress → briefing appears with charts, citations, follow-ups, and report findings saved.
Read the output file to retrieve the result: /private/tmp/claude-501/-Users-shaynelarocque-Documents-GitHub-futuresight/5c8423a6-db28-4eb0-98eb-ccb8759929ff/tasks/bloazztof.output