Update app.py

app.py

@@ -1,4 +1,4 @@
-# app.py -- HF-ready single-server FastAPI + Gradio mounted app (fixed)
+# app.py -- HF-ready single-server FastAPI + Gradio mounted app (no double server conflict)
 import os
 import shutil
 import asyncio
@@ -9,10 +9,10 @@ from fastapi.responses import JSONResponse
 import gradio as gr
 import uvicorn
 
-# Import your real multimodal module
+# Import your multimodal module
 from multimodal_module import MultiModalChatModule
 
-# Instantiate your AI module
+# Instantiate AI module
 AI = MultiModalChatModule()
 
 # ---------- Helpers ----------
@@ -20,7 +20,7 @@ TMP_DIR = "/tmp"
 os.makedirs(TMP_DIR, exist_ok=True)
 
 class FileWrapper:
-    """Simple path wrapper compatible with your existing code expectations."""
+    """Simple path wrapper for AI methods."""
     def __init__(self, path: str):
         self._path = path
 
@@ -29,52 +29,42 @@ class FileWrapper:
         await loop.run_in_executor(None, shutil.copyfile, self._path, dst_path)
 
 async def save_upload_to_tmp(up: UploadFile) -> str:
-    """Save FastAPI UploadFile to /tmp and return path. Uses async read."""
+    """Save FastAPI UploadFile to /tmp and return path."""
     if not up or not up.filename:
-        raise ValueError("UploadFile missing or has no filename")
+        raise ValueError("No file uploaded")
     dest = os.path.join(TMP_DIR, up.filename)
-    data = await up.read()              # <-- important: async read
+    data = await up.read()
     with open(dest, "wb") as f:
         f.write(data)
     return dest
 
 async def call_ai(fn, *args, **kwargs):
-    """
-    Call AI functions safely: if fn is async, await it; if sync, run in thread.
-    If fn is None, raise a clear error.
-    """
+    """Run AI method whether it's sync or async."""
     if fn is None:
-        raise AttributeError("Requested AI method is not implemented in multimodal_module")
+        raise AttributeError("Requested AI method not implemented")
     if inspect.iscoroutinefunction(fn):
         return await fn(*args, **kwargs)
-    else:
-        return await asyncio.to_thread(lambda: fn(*args, **kwargs))
+    return await asyncio.to_thread(lambda: fn(*args, **kwargs))
 
-# ---------- FastAPI app ----------
+# ---------- FastAPI ----------
 app = FastAPI(title="Multimodal Module API")
 
-# Optional: allow CORS if external web apps will call this
+# CORS (if you call this from the browser)
 from fastapi.middleware.cors import CORSMiddleware
 app.add_middleware(
     CORSMiddleware,
-    allow_origins=["*"],  # tighten in production
+    allow_origins=["*"],  # tighten for prod
     allow_credentials=True,
     allow_methods=["*"],
     allow_headers=["*"],
 )
 
 # ----------------- API endpoints -----------------
-
 @app.post("/api/predict")
 async def api_predict(inputs: str = Form(...), user_id: Optional[int] = Form(0), lang: str = Form("en")):
-    """
-    HuggingFace-style /predict compatibility.
-    Form field 'inputs' used as text.
-    """
     try:
         fn = getattr(AI, "generate_response", getattr(AI, "process_text", None))
         reply = await call_ai(fn, inputs, int(user_id), lang)
-        # HF-style returns "data" array
         return {"data": [reply]}
     except Exception as e:
         return JSONResponse({"error": str(e)}, status_code=500)
@@ -90,9 +80,6 @@ async def api_text(text: str = Form(...), user_id: Optional[int] = Form(0), lang
 
 @app.post("/api/voice")
 async def api_voice(user_id: Optional[int] = Form(0), audio_file: UploadFile = File(...)):
-    """
-    Upload audio file (multipart/form-data). Returns whatever your AI.process_voice_message returns (JSON/dict).
-    """
     try:
         path = await save_upload_to_tmp(audio_file)
         fn = getattr(AI, "process_voice_message", None)
@@ -166,8 +153,6 @@ async def api_file(user_id: Optional[int] = Form(0), file_obj: UploadFile = File
 async def api_code(user_id: Optional[int] = Form(0), prompt: str = Form(...), max_tokens: int = Form(512)):
     try:
         fn = getattr(AI, "code_complete", None)
-        # Many implementations expect (user_id, prompt, max_tokens) or (prompt, max_tokens)
-        # Try user-first signature first, fallback to prompt-first
         try:
             result = await call_ai(fn, int(user_id), prompt, max_tokens)
         except TypeError:
@@ -176,15 +161,13 @@ async def api_code(user_id: Optional[int] = Form(0), prompt: str = Form(...), ma
     except Exception as e:
         return JSONResponse({"error": str(e)}, status_code=500)
 
-# ---------- Minimal Gradio UI (mounted) ----------
+# ---------- Minimal Gradio UI ----------
 def gradio_text_fn(text, user_id, lang):
     fn = getattr(AI, "generate_response", getattr(AI, "process_text", None))
     if fn is None:
-        return "Error: text handler not implemented in multimodal_module"
-    if inspect.iscoroutinefunction(fn):
-        return asyncio.run(call_ai(fn, text, int(user_id or 0), lang))
-    else:
-        return fn(text, int(user_id or 0), lang)
+        return "Error: text handler not implemented"
+    loop = asyncio.get_event_loop()
+    return loop.run_until_complete(call_ai(fn, text, int(user_id or 0), lang))
 
 with gr.Blocks(title="Multimodal Bot (UI)") as demo:
     gr.Markdown("# 🧠 Multimodal Bot — UI")
@@ -195,10 +178,10 @@ with gr.Blocks(title="Multimodal Bot (UI)") as demo:
     out = gr.Textbox(lines=6, label="Reply")
     gr.Button("Send").click(gradio_text_fn, [inp, txt_uid, txt_lang], out)
 
-# Mount Gradio app at root
+# Mount Gradio at /
 app = gr.mount_gradio_app(app, demo, path="/")
 
-# ---------- Run server (HF Spaces uses this entrypoint) ----------
+# ---------- Run ----------
 if __name__ == "__main__":
     port = int(os.environ.get("PORT", 7860))
-    uvicorn.run(app, host="0.0.0.0", port=port)
+    uvicorn.run("app:app", host="0.0.0.0", port=port, reload=False)