Update app.py

app.py

@@ -1,119 +1,135 @@
-import os
-import shutil
-from fastapi.responses import FileResponse
-import asyncio
-from fastapi import FastAPI, UploadFile, File, Form, HTTPException, BackgroundTasks
-from fastapi.responses import StreamingResponse
-from pydantic import BaseModel
-from utils import STT, TTS
-from data_ingestion import Ingest_Data 
-from RAG import app as rag_app, Ragbot_State, reload_vector_store
-
-# Initialize FastAPI
-app = FastAPI(title="LangGraph RAG Chatbot", version="1.0")
-
-# --- Pydantic Models ---
-class ChatRequest(BaseModel):
-    query: str
-    thread_id: str = "default_user"
-    use_rag: bool = False
-    use_web: bool = False
-    model_name: str = "gpt"
-
-class TTSRequest(BaseModel):
-    text: str
-    voice: str = "en-US-AriaNeural"
-
-
-# --- Endpoints ---
-
-@app.get("/")
-def health_check():
-    return {"status": "running", "message": "Bot is ready"}
-
-@app.post("/upload")
-async def upload_document(
-    file: UploadFile = File(...), 
-    background_tasks: BackgroundTasks = BackgroundTasks()
-):
-    try:
-        temp_filename = f"temp_{file.filename}"
-
-        with open(temp_filename, "wb") as buffer:
-            shutil.copyfileobj(file.file, buffer)
-
-        def process_and_reload(path):
-            try:
-                result = Ingest_Data(path)
-                print(f"Ingestion Result: {result}")
-                reload_vector_store()
-                
-            except Exception as e:
-                print(f"Error processing background task: {e}")
-            finally:
-                if os.path.exists(path):
-                    os.remove(path)
-
-        background_tasks.add_task(process_and_reload, temp_filename)
-
-        return {
-            "message": "File received. Processing started in background.", 
-            "filename": file.filename
-        }
-        
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-
-@app.post("/chat")
-async def chat_endpoint(request: ChatRequest):
-    config = {"configurable": {"thread_id": request.thread_id}}
-    
-    inputs = {
-        "query": request.query,
-        "RAG": request.use_rag,
-        "web_search": request.use_web,
-        "model_name": request.model_name,
-        "context": [],
-        "metadata": [],
-        "web_context": "",
-    }
-
-    async def event_generator():
-        async for event in rag_app.astream_events(inputs, config=config, version="v1"):
-            kind = event["event"]
-            if kind == "on_chat_model_stream":
-                content = event["data"]["chunk"].content
-                
-                if content:
-                    data = content.replace("\n", "\\n")
-                    yield f"data: {data}\n\n"
-
-    return StreamingResponse(
-        event_generator(), 
-        media_type="text/event-stream",
-        headers={
-            "Cache-Control": "no-cache",
-            "Connection": "keep-alive",
-            "X-Accel-Buffering": "no",
-        },
-    )
-
-
-# ---------------- STT ---------------- #
-@app.post("/stt")
-async def transcribe_audio(file: UploadFile = File(...)):
-    try:
-        return await STT(file)
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
-
-# ---------------- TTS ---------------- #
-@app.post("/tts")
-async def text_to_speech(req: TTSRequest):
-    try:
-        audio_path = await TTS(req.text, req.voice)
-        return FileResponse(audio_path, media_type="audio/mpeg", filename="output.mp3")
-        
-    except Exception as e:
-        raise HTTPException(status_code=500, detail=str(e))
+import os
+import shutil
+from fastapi.responses import FileResponse
+import asyncio
+from fastapi import FastAPI, UploadFile, File, Form, HTTPException, BackgroundTasks
+from fastapi.responses import StreamingResponse
+from pydantic import BaseModel
+from utils import STT, TTS
+from data_ingestion import Ingest_Data 
+from RAG import app as rag_app, Ragbot_State, reload_vector_store
+
+# Initialize FastAPI
+app = FastAPI(title="LangGraph RAG Chatbot", version="1.0")
+
+# --- Pydantic Models ---
+class ChatRequest(BaseModel):
+    query: str
+    thread_id: str = "default_user"
+    use_rag: bool = False
+    use_web: bool = False
+    model_name: str = "gpt"
+
+class TTSRequest(BaseModel):
+    text: str
+    voice: str = "en-US-AriaNeural"
+
+
+# --- Endpoints ---
+
+@app.get("/")
+def health_check():
+    return {"status": "running", "message": "Bot is ready"}
+
+@app.post("/upload")
+async def upload_document(
+    file: UploadFile = File(...), 
+    background_tasks: BackgroundTasks = BackgroundTasks()
+):
+    try:
+        temp_filename = f"temp_{file.filename}"
+
+        with open(temp_filename, "wb") as buffer:
+            shutil.copyfileobj(file.file, buffer)
+
+        def process_and_reload(path):
+            try:
+                result = Ingest_Data(path)
+                print(f"Ingestion Result: {result}")
+                reload_vector_store()
+                
+            except Exception as e:
+                print(f"Error processing background task: {e}")
+            finally:
+                if os.path.exists(path):
+                    os.remove(path)
+
+        background_tasks.add_task(process_and_reload, temp_filename)
+
+        return {
+            "message": "File received. Processing started in background.", 
+            "filename": file.filename
+        }
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+@app.post("/chat")
+async def chat_endpoint(request: ChatRequest):
+    """
+    Streaming endpoint adapted from your working Hugging Face snippet.
+    """
+    # 1. Setup Inputs
+    config = {"configurable": {"thread_id": request.thread_id}}
+    
+    inputs = {
+        "query": request.query,
+        "RAG": request.use_rag,
+        "web_search": request.use_web,
+        "model_name": request.model_name,
+        "context": [],
+        "metadata": [],
+        "web_context": "",
+    }
+
+    # 2. Define the Generator (Matching your snippet's logic)
+    async def event_generator():
+        # Iterate through events (LangGraph's version of bot.stream)
+        async for event in rag_app.astream_events(inputs, config=config, version="v1"):
+            
+            # We look for the specific event type that contains the LLM chunks
+            kind = event["event"]
+            
+            if kind == "on_chat_model_stream":
+                # Get the chunk data
+                chunk = event["data"]["chunk"]
+                
+                # Logic from your snippet: check if content exists
+                if chunk and hasattr(chunk, "content"):
+                    content = chunk.content
+                    
+                    if content:
+                        # EXACT FORMATTING FROM YOUR SNIPPET
+                        data = str(content).replace("\n", "\\n")
+                        yield f"data: {data}\n\n"
+
+    # 3. Return StreamingResponse (Matching your snippet's headers)
+    return StreamingResponse(
+        event_generator(),
+        media_type="text/event-stream",
+        headers={
+            "Cache-Control": "no-cache",
+            "X-Accel-Buffering": "no", # Critical for Hugging Face
+            "Connection": "keep-alive", # Added for extra safety
+        },
+    )
+
+
+# ---------------- STT ---------------- #
+@app.post("/stt")
+async def transcribe_audio(file: UploadFile = File(...)):
+    try:
+        return await STT(file)
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+# ---------------- TTS ---------------- #
+@app.post("/tts")
+async def text_to_speech(req: TTSRequest):
+    try:
+        audio_path = await TTS(req.text, req.voice)
+        return FileResponse(audio_path, media_type="audio/mpeg", filename="output.mp3")
+        
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))