Update app.py

app.py

@@ -3,6 +3,7 @@ import torch
 import torch.nn as nn
 import sys
 import os
+import uvicorn
 
 from fastapi import FastAPI, UploadFile, File
 from fastapi.middleware.cors import CORSMiddleware
@@ -37,7 +38,6 @@ REG_HEADS    = 8
 REG_DROPOUT  = 0.1
 PROJ_DROPOUT = 0.1
 
-# ── Checkpoint loader helper ───────────────────────────────────────────────────
 def _unwrap_ckpt(ckpt):
     if not isinstance(ckpt, dict):
         return ckpt
@@ -52,14 +52,12 @@ def _load_ckpt(model, path):
     model.load_state_dict(state)
     return model
 
-# ── Visual model ───────────────────────────────────────────────────────────────
 print("⏳ Loading visual model...")
 visual_model = VisualPersonalityModel(VISUAL_CFG).to(DEVICE)
 visual_model = _load_ckpt(visual_model, "checkpoints/best_model.pt")
 visual_model.eval()
 print("✓ Visual model ready")
 
-# ── Fusion model ───────────────────────────────────────────────────────────────
 print("⏳ Loading fusion model...")
 fusion_model = FusionMLP(
     in_dim=1024, hidden_dims=[512, 256], num_traits=5, dropout=0.3
@@ -68,7 +66,6 @@ fusion_model = _load_ckpt(fusion_model, "checkpoints/best_fusion_model.pt")
 fusion_model.eval()
 print("✓ Fusion model ready")
 
-# ── Audio model ────────────────────────────────────────────────────────────────
 print("⏳ Loading audio model...")
 frozen_ext = FrozenExtractor("facebook/wav2vec2-base", n_shallow=N_SHALLOW).to(DEVICE)
 frozen_ext.eval()
@@ -112,13 +109,11 @@ for m in [deep_transformer, proj, agru, regressor]:
     m.eval()
 print("✓ Audio model ready")
 
-# ── Face detector ──────────────────────────────────────────────────────────────
 print("⏳ Loading face detector...")
 face_detector = _load_face_detector()
 print("✓ Face detector ready")
 
 
-# ── CORE LOGIC HELPER ──────────────────────────────────────────────────────────
 def process_video_bytes(video_bytes):
     visual_preds, visual_emb = get_visual_embedding(video_bytes, visual_model, DEVICE, face_detector)
     audio_preds, audio_emb = get_audio_embedding(video_bytes, frozen_ext, deep_transformer, proj, agru, regressor, DEVICE)
@@ -127,10 +122,9 @@ def process_video_bytes(video_bytes):
     return results
 
 
-# ── FASTAPI SETUP (FOR REACT) ──────────────────────────────────────────────────
 app = FastAPI()
 
-# THE MAGIC BULLET FOR CORS: allow_origins=["*"] MUST HAVE allow_credentials=False
+# Wide open CORS to completely eliminate React local testing errors
 app.add_middleware(
     CORSMiddleware,
     allow_origins=["*"],
@@ -151,7 +145,6 @@ async def api_predict(video: UploadFile = File(...)):
         return JSONResponse(content={"error": str(e), "trace": traceback.format_exc()}, status_code=500)
 
 
-# ── GRADIO UI SETUP (FOR MANUAL TESTING) ───────────────────────────────────────
 def gradio_predict(video_path):
     if video_path is None:
         return None, "⚠️ Please upload a video first."
@@ -160,8 +153,8 @@ def gradio_predict(video_path):
             video_bytes = f.read()
         
         results = process_video_bytes(video_bytes)
-        
         fusion = results["fusion"]
+        
         md = "## 🧠 Fusion Results (Main)\n\n"
         for trait, score in fusion.items():
             bar = "█" * int(score / 5) + "░" * (20 - int(score / 5))
@@ -189,4 +182,7 @@ with gr.Blocks(title="Personality Prediction API") as demo:
     )
 
 # Mount Gradio over the FastAPI app
-app = gr.mount_gradio_app(app, demo, path="/")
+app = gr.mount_gradio_app(app, demo, path="/")
+
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)