Update app.py

app.py

@@ -120,34 +120,40 @@ def compute_embedding(title, description, tags, thumbnail_url):
         fused = attn_out.squeeze(1)
         return fused.squeeze(0).cpu().numpy().tolist()  # Note: This is proj_dim=768, not 1—adjust if regression output
 
-# --- FastAPI + Gradio (unchanged) ---
-app = FastAPI()
-
-@app.post("/api/get_embedding")
-async def api_get_embedding(request: Request):
-    data = await request.json()
-    emb = compute_embedding(
-        data.get("title", ""), data.get("description", ""),
-        data.get("tags", ""), data.get("thumbnail_url", "")
-    )
-    return JSONResponse({"embedding": emb})
+# --- Keep everything up to compute_embedding() unchanged ---
+# (Imports, config, model loading, transform, compute_embedding)
 
+# --- NEW: Pure Gradio Interface + Launch (remove FastAPI entirely) ---
 def gradio_fn(title, description, tags, thumbnail_url):
     emb = compute_embedding(title, description, tags, thumbnail_url)
-    return f"Embedding length={len(emb)}; first 10: {emb[:10]}"
+    return f"Embedding length={len(emb)}; first 10: {emb[:10]}\nFull embedding: {emb}"
 
-gr_interface = gr.Interface(
+# Create the interface (same as before)
+demo = gr.Interface(  # Renamed from gr_interface for clarity
     fn=gradio_fn,
-    inputs=[gr.Textbox(label="Title"), gr.Textbox(label="Description"),
-            gr.Textbox(label="Tags"), gr.Textbox(label="Thumbnail URL")],
-    outputs="text",
+    inputs=[
+        gr.Textbox(label="Title", placeholder="Enter video title..."),
+        gr.Textbox(label="Description", placeholder="Enter video description..."),
+        gr.Textbox(label="Tags", placeholder="Enter comma-separated tags..."),
+        gr.Textbox(label="Thumbnail URL", placeholder="Enter image URL (e.g., https://example.com/thumb.jpg)...")
+    ],
+    outputs=gr.Textbox(label="Generated Embedding", lines=10),
     title="Video Embedding Generator",
-    description="Generates fused multimodal embeddings from video metadata",
+    description="Generates fused multimodal embeddings from video metadata using LaBSE + ViT + Custom Fusion.",
+    examples=[  # Optional: Add sample inputs for easy testing
+        ["Test Video", "A sample description.", "ml, ai, video", "https://via.placeholder.com/224"]
+    ],
+    allow_flagging="never"  # Disable if not needed
 )
 
-app = gr.mount_gradio_app(app, gr_interface, path="/")
-
-# NEW: Launch the server (blocks until shutdown)
+# NEW: Launch directly (blocks and serves everything)
 if __name__ == "__main__":
-    import uvicorn
-    uvicorn.run(app, host="0.0.0.0", port=7860, log_level="info")
+    demo.queue(max_size=10)  # Enable queuing for concurrency (HF handles 10+ users)
+    demo.launch(
+        server_name="0.0.0.0",
+        server_port=7860,
+        share=False,  # HF provides public URL, no need for temp share
+        show_api=True,  # Exposes /api endpoint automatically (see below)
+        debug=False,   # Set True for more logs during testing
+        favicon_path=None  # Optional: Add custom favicon later
+    )