New Try

app.py

@@ -7,10 +7,12 @@ import time
 import os
 import json
 from typing import Dict, List, Any
-from fastapi import FastAPI, UploadFile, File, Form, HTTPException
-from fastapi.responses import JSONResponse
+from fastapi import FastAPI, UploadFile, File, HTTPException
+from fastapi.responses import JSONResponse, HTMLResponse
 import uuid
 from pathlib import Path
+import gradio as gr
+import tempfile
 
 app = FastAPI()
 
@@ -304,39 +306,106 @@ def process_video(video_path: str) -> Dict[str, Any]:
         "error": None
     }
 
-@app.post("/analyze-video")
+# Gradio Interface Functions
+def gradio_analyze_video(video_path: str):
+    """Wrapper function for Gradio interface"""
+    result = process_video(video_path)
+    if not result["success"]:
+        return {"error": result.get("error", "Processing failed")}
+    
+    # Format results for better Gradio display
+    summary = result["results"]["summary"]
+    detections = result["results"]["detections"]
+    
+    output = {
+        "summary": {
+            "total_frames": summary["total_frames"],
+            "faces_detected": summary["total_detections"],
+            "dominant_emotion": summary["dominant_emotion"],
+            "emotion_distribution": summary["emotions_count"]
+        },
+        "sample_detections": detections[:5]  # Show first 5 detections
+    }
+    return output
+
+def save_upload_file_tmp(upload_file: UploadFile) -> str:
+    """Save uploaded file to temporary location"""
+    try:
+        suffix = Path(upload_file.filename).suffix
+        with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
+            tmp.write(upload_file.file.read())
+            return tmp.name
+    finally:
+        upload_file.file.close()
+
+# Gradio Interface
+with gr.Blocks(title="Video Emotion Detection", theme=gr.themes.Soft()) as demo:
+    gr.Markdown("""
+    # 🎭 Video Emotion Detection
+    Upload a video to analyze facial emotions frame by frame
+    """)
+    
+    with gr.Row():
+        with gr.Column():
+            video_input = gr.Video(
+                label="Upload Video", 
+                sources=["upload"],
+                type="filepath"
+            )
+            submit_btn = gr.Button("Analyze Video", variant="primary")
+            
+        with gr.Column():
+            output_json = gr.JSON(label="Analysis Results")
+            gr.Markdown("""
+            ### Results Interpretation
+            - **Dominant Emotion**: Most frequently detected emotion
+            - **Emotion Distribution**: Count of each emotion detected
+            - **Sample Detections**: First 5 emotion detections
+            """)
+    
+    submit_btn.click(
+        fn=gradio_analyze_video,
+        inputs=video_input,
+        outputs=output_json,
+        api_name="predict"
+    )
+
+# FastAPI Endpoints
+@app.post("/api/analyze-video")
 async def analyze_video(file: UploadFile = File(...)):
+    """Original FastAPI endpoint"""
     try:
-        # Create uploads directory if it doesn't exist
-        upload_dir = Path("uploads")
-        upload_dir.mkdir(exist_ok=True)
-        
-        # Generate unique filename
-        file_ext = file.filename.split(".")[-1]
-        temp_filename = f"{uuid.uuid4()}.{file_ext}"
-        temp_path = upload_dir / temp_filename
-        
-        # Save the uploaded file
-        with open(temp_path, "wb") as buffer:
-            buffer.write(await file.read())
-        
-        # Process the video
-        result = process_video(str(temp_path))
-        
-        # Clean up - remove the temporary file
-        os.remove(temp_path)
+        temp_path = save_upload_file_tmp(file)
+        result = process_video(temp_path)
+        os.unlink(temp_path)
         
         if not result["success"]:
             raise HTTPException(status_code=400, detail=result.get("error", "Processing failed"))
-        
         return JSONResponse(content=result)
     
     except Exception as e:
-        # Clean up if file was created
         if 'temp_path' in locals() and os.path.exists(temp_path):
-            os.remove(temp_path)
+            os.unlink(temp_path)
         raise HTTPException(status_code=500, detail=str(e))
 
+@app.get("/", response_class=HTMLResponse)
+async def root():
+    """Redirect root to Gradio interface"""
+    return """
+    <html>
+        <head>
+            <title>Video Emotion Detection</title>
+            <meta http-equiv="refresh" content="0; url=/gradio" />
+        </head>
+        <body>
+            <p>Redirecting to Gradio interface... <a href="/gradio">Click here</a> if not redirected.</p>
+        </body>
+    </html>
+    """
+
+# Mount Gradio app to FastAPI
+app = gr.mount_gradio_app(app, demo, path="/gradio")
+
 if __name__ == "__main__":
     import uvicorn
     uvicorn.run(app, host="0.0.0.0", port=7860)

requirements.txt

@@ -5,4 +5,5 @@ torchvision
 opencv-python
 numpy
 Pillow
-python-multipart
+python-multipart
+gradio