feat: Add meeting transcript analysis API endpoint and service powered by Gemini, and ignore venv.

Author: rahul-aot

.gitignore

@@ -60,6 +60,8 @@ dist/
 .next/
 out/
 
+venv
+
 # Docker
 *.env.docker
 

backend/app/api/analyze.py

@@ -16,7 +16,8 @@ def analyze_meeting(payload: TranscriptRequest):
         return analyze_transcript(payload.transcript)
     except ValueError as exc:
         raise HTTPException(status_code=400, detail=str(exc))
-    except Exception:
+    except Exception as exc:
+        print("Exception", exc)
         raise HTTPException(
             status_code=500,
             detail="Failed to analyze meeting transcript"

backend/app/services/analyzer.py

@@ -15,65 +15,38 @@
 def analyze_transcript(transcript: str) -> MeetingAnalysisResponse:
     """
     Analyze meeting transcript and return structured summary,
-    decisions, and action items.
+    decisions, and action items using Gemini's native structured output.
     """
 
     transcript = clean_text(transcript)
 
     if not is_valid_transcript(transcript):
         raise ValueError("Transcript content is too short or invalid")
 
-    system_prompt = """
-You are an AI assistant that analyzes meeting transcripts.
-
-Your task:
-- Produce a concise summary
-- Extract key decisions
-- Extract action items with owner and priority if mentioned
-
-STRICT RULES:
-- Respond ONLY in valid JSON
-- Do NOT include explanations
-- Do NOT add extra fields
-- If information is missing, use null or empty lists
-
-JSON FORMAT:
-{
-  "summary": "string",
-  "decisions": ["string"],
-  "action_items": [
-    {
-      "task": "string",
-      "owner": "string | null",
-      "priority": "High | Medium | Low | null"
-    }
-  ]
-}
-"""
+    system_instruction = (
+        "You are an expert meeting analyst. Your task is to provide a concise summary, "
+        "identify key decisions made, and list action items with their respective "
+        "owners and priority levels."
+    )
 
-    response = client.models.generate_content(
-        model=settings.GEMINI_MODEL,
-        contents=[
-            types.Content(
-                role="user",
-                parts=[
-                    types.Part(text=system_prompt),
-                    types.Part(text=f"\nTRANSCRIPT:\n{transcript}")
-                ]
+    try:
+        # 3. Generate Content with Native Schema Support
+        response = client.models.generate_content(
+            model=settings.GEMINI_MODEL,
+            contents=f"TRANSCRIPT:\n{transcript}",
+            config=types.GenerateContentConfig(
+                system_instruction=system_instruction,
+                temperature=0.2,
+                response_mime_type="application/json",
+                # Passing the Pydantic class directly enables native structured output
+                response_schema=MeetingAnalysisResponse,
             )
-        ],
-        generation_config=types.GenerationConfig(
-            temperature=0.2,
-            response_mime_type="application/json"
         )
-    )
 
-    raw_text = response.text.strip()
-
-    try:
-        data: Dict[str, Any] = json.loads(raw_text)
-    except json.JSONDecodeError as exc:
-        raise ValueError("Gemini returned invalid JSON") from exc
+        if response.parsed:
+            return response.parsed
+            
+        return MeetingAnalysisResponse.model_validate_json(response.text)
 
-    # Validate against Pydantic schema
-    return MeetingAnalysisResponse(**data)
+    except Exception as exc:
+        raise ValueError(f"Gemini analysis failed: {str(exc)}") from exc