Fix: Implement /analyze_topic endpoint to handle calls from Gradio UI

modal_whisper_app.py

@@ -489,46 +489,36 @@ async def analyze_topic_endpoint(topic: str = Query(..., min_length=3, descripti
                                  max_videos: Optional[int] = Query(3, ge=1, le=10, description="Maximum number of videos to find and analyze.")):
     """Endpoint to find videos for a topic, analyze them, and return aggregated results."""
     print(f"[FastAPI /analyze_topic] Received request for topic: '{topic}', max_videos: {max_videos}")
-    
-    # This endpoint is orchestrated by Cascade. Cascade will:
-    # 1. Call its `search_web` tool with the `topic`.
-    # 2. Call the local Python helper `extract_video_urls_from_search` with the search results.
-    # 3. Call the Modal function `analyze_videos_by_topic.remote()` with the extracted URLs and topic.
-    # The actual implementation of these steps happens in Cascade's execution flow, not directly in this FastAPI code.
-    # This FastAPI endpoint definition tells Modal to expect such a route and parameters.
-    # The body of this function in the Python file is a placeholder for Cascade's orchestration.
-
-    # Placeholder: The actual call to analyze_videos_by_topic.remote() will be made by Cascade
-    # after it performs the search and URL extraction.
-    # For standalone Modal testing, one might simulate this: 
-    # if modal.is_local():
-    #     # Simulate search and extraction
-    #     simulated_search_results = [{"link": "https://www.youtube.com/watch?v=dQw4w9WgXcQ"}]
-    #     video_urls = extract_video_urls_from_search(simulated_search_results, max_videos)
-    #     if not video_urls:
-    #         return JSONResponse(status_code=404, content={"error": "No relevant video URLs found for the topic after search."})
-    #     try:
-    #         result = await analyze_videos_by_topic.coro(video_urls=video_urls, topic=topic)
-    #         return result
-    #     except Exception as e:
-    #         return JSONResponse(status_code=500, content={"error": f"Error during topic analysis: {str(e)}"})
-    # else:
-    #     # In deployed Modal, Cascade handles the call chain.
-    #     # This function body might not even be executed directly if Cascade calls .remote() on analyze_videos_by_topic directly.
-    #     # However, having a defined endpoint is good practice for discoverability and potential direct calls.
-    pass
-
-    # The actual logic is: Cascade calls search_web -> Cascade calls extract_video_urls_from_search -> Cascade calls analyze_videos_by_topic.remote().
-    # This endpoint is the entry point for the USER's request to Cascade.
-    # Cascade will then perform the sequence of operations.
-    # So, this function body is more of a declaration for the endpoint.
-    # We expect Cascade to handle the full orchestration when this endpoint is invoked.
-    # For the purpose of defining the Modal app structure, this is sufficient.
-    # The `analyze_videos_by_topic` function is what Modal will ultimately run with the list of URLs provided by Cascade.
-    
-    # Return a message indicating that the process is initiated by Cascade.
-    # This response won't typically be seen if Cascade directly calls the .remote() of the target function.
-    return JSONResponse(status_code=202, content={"message": "Topic analysis process initiated. Cascade will orchestrate the search and analysis."})
+    try:
+        print(f"[FastAPI /analyze_topic] Calling find_video_urls_for_topic for topic: '{topic}', max_videos: {max_videos}")
+        # find_video_urls_for_topic is a Modal function that internally calls search_web and extracts URLs
+        video_urls_dict = await find_video_urls_for_topic.call(topic=topic, max_results=max_videos)
+        
+        if "error" in video_urls_dict:
+            print(f"[FastAPI /analyze_topic] Error from find_video_urls_for_topic: {video_urls_dict}")
+            # Use status_code from the error dict if available, otherwise default (e.g., 500)
+            return JSONResponse(status_code=video_urls_dict.get("status_code", 500), content=video_urls_dict)
+
+        video_urls = video_urls_dict.get("video_urls", [])
+
+        if not video_urls:
+            print(f"[FastAPI /analyze_topic] No video URLs found by find_video_urls_for_topic for topic: '{topic}'")
+            return JSONResponse(status_code=404, content={"error": "No relevant video URLs found for the topic after search.", "details": video_urls_dict.get("search_provider_log", "No additional search details.")})
+        
+        print(f"[FastAPI /analyze_topic] Found {len(video_urls)} URLs: {video_urls}. Calling analyze_videos_by_topic.")
+        # analyze_videos_by_topic is another Modal function
+        analysis_results = await analyze_videos_by_topic.call(video_urls=video_urls, topic=topic)
+        print(f"[FastAPI /analyze_topic] Analysis complete for topic: '{topic}'.")
+        return JSONResponse(status_code=200, content=analysis_results)
+
+    except modal.exception.ModalError as e:
+        print(f"[FastAPI /analyze_topic] ModalError during topic analysis: {e}")
+        return JSONResponse(status_code=500, content={"error": f"Modal processing error during topic analysis: {str(e)}"})
+    except Exception as e:
+        print(f"[FastAPI /analyze_topic] Unexpected Exception during topic analysis: {e}")
+        # import traceback # For server-side debugging
+        # traceback.print_exc() # For server-side debugging
+        return JSONResponse(status_code=500, content={"error": f"Unexpected server error during topic analysis: {str(e)}"})