Update app.py

app.py

@@ -1,4 +1,13 @@
 from fastai.vision.all import *
+from fastapi import FastAPI, HTTPException
+from fastapi.middleware.cors import CORSMiddleware
+import uvicorn
+import logging
+import tempfile
+from pathlib import Path
+import firebase_admin
+from firebase_admin import credentials, firestore, storage
+from pydantic import BaseModel
 
 # Load the pre-trained model
 learn = load_learner('model.pkl')
@@ -9,7 +18,85 @@ searches = sorted(searches)  # Ensure the categories are in sorted order
 values = [i for i in range(0, len(searches))]
 class_dict = dict(zip(searches, values))
 
-def classify_image(image_path):
+# Set up logging
+logging.basicConfig(level=logging.DEBUG,
+                    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
+logger = logging.getLogger(__name__)
+
+# Initialize Firebase
+try:
+    cred = credentials.Certificate("serviceAccountKey.json")
+    firebase_app = firebase_admin.initialize_app(cred, {
+        'storageBucket': 'future-forge-60d3f.appspot.com'
+    })
+    db = firestore.client()
+    bucket = storage.bucket(app=firebase_app)
+    logger.info("Firebase initialized successfully")
+except Exception as e:
+    logger.error(f"Failed to initialize Firebase: {str(e)}")
+
+app = FastAPI()
+
+# Add CORS middleware
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+
+# Define the input model
+class FileProcess(BaseModel):
+    file_path: str
+
+
+@app.post("/process")
+async def process_file(file_data: FileProcess):
+    logger.info(f"Processing file from Firebase Storage: {file_data.file_path}")
+
+    try:
+        # Get the file from Firebase Storage
+        blob = bucket.blob(file_data.file_path)
+
+        # Create a temporary file
+        with tempfile.NamedTemporaryFile(delete=False, suffix=f".{file_data.file_path.split('.')[-1]}") as tmp_file:
+            blob.download_to_filename(tmp_file.name)
+            tmp_file_path = Path(tmp_file.name)
+
+        logger.info(f"File downloaded temporarily at: {tmp_file_path}")
+
+        file_type = file_data.file_path.split('.')[-1].lower()
+
+        try:
+            if file_type in ['jpg', 'jpeg', 'png', 'bmp']:
+                output = process_video(str(tmp_file_path))
+                result = {"type": "image", "data": {"result": output}}
+            else:
+                raise HTTPException(status_code=400, detail="Unsupported file type")
+
+            logger.info(f"Processing complete. Result: {result}")
+
+            # Store result in Firebase
+            try:
+                doc_ref = db.collection('results').add(result)
+                return {"message": "File processed successfully", "result": result}
+            except Exception as e:
+                logger.error(f"Failed to store result in Firebase: {str(e)}")
+                return {"message": "File processed successfully, but failed to store in Firebase", "result": result,
+                        "error": str(e)}
+
+        finally:
+            # Clean up the temporary file
+            tmp_file_path.unlink()
+
+    except Exception as e:
+        logger.error(f"Error processing file: {str(e)}")
+        raise HTTPException(status_code=500, detail=f"Error processing file: {str(e)}")
+
+
+def process_video(video_path):
     # Load the image from the provided path
     img = PILImage.create(image_path)
     
@@ -26,7 +113,7 @@ def classify_image(image_path):
     else:
         return {'formal': confidences['formal']}
 
-# Example usage with an image path
-image_path = 'test1.png'  # Replace with the actual image path
-result = classify_image(image_path)
-print(result)
+
+if __name__ == "__main__":
+    logger.info("Starting the Face Emotion Recognition API")
+    uvicorn.run(app, host="0.0.0.0", port=8000)