Update app.py

app.py

@@ -3,15 +3,16 @@ import google.generativeai as genai
 import os
 import tempfile
 import base64
+import logging
 from dotenv import load_dotenv
 
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+
 # Load environment variables
 load_dotenv()
 
-# Configure Flask app
 app = Flask(__name__)
-
-# Configure Gemini API
 genai.configure(api_key=os.getenv("GEMINI_API_KEY"))
 
 @app.route("/")
@@ -21,70 +22,69 @@ def home():
 @app.route("/process", methods=["POST"])
 def process_image():
     try:
-        # Get data from request
         data = request.json
         image_data = data.get("image")
-        object_type = data.get("objectType")
+        object_type = data.get("objectType", "").strip()
 
+        # Validate inputs
         if not image_data or not object_type:
-            return jsonify({"success": False, "message": "Invalid input data"})
-
-        # Decode base64 image data
-        image_bytes = base64.b64decode(image_data.split(",")[1])
-
-        # Create temporary directory
-        temp_dir = tempfile.mkdtemp()
-        input_path = os.path.join(temp_dir, "input.png")
-        with open(input_path, "wb") as f:
-            f.write(image_bytes)
-
-        # Create the model
-        model = genai.GenerativeModel('gemini-2.0-flash-exp-image-generation')
-
-        # Build the prompt
-        prompt = f"Remove the {object_type} from the image and fill the area naturally."
-
-        # Disable all safety settings
-        safety_settings = {
-            "HARM_CATEGORY_HARASSMENT": "BLOCK_NONE",
-            "HARM_CATEGORY_HATE_SPEECH": "BLOCK_NONE",
-            "HARM_CATEGORY_SEXUALLY_EXPLICIT": "BLOCK_NONE",
-            "HARM_CATEGORY_DANGEROUS_CONTENT": "BLOCK_NONE",
-            "HARM_CATEGORY_CIVIC_INTEGRITY": "BLOCK_NONE"
-        }
-
-        # Generate content
-        response = model.generate_content(
-            [
-                prompt,
-                genai.upload_file(input_path)
-            ],
-            generation_config={
-                "temperature": 1,
-                "top_p": 0.95,
-                "top_k": 40,
-                "max_output_tokens": 8192,
-            },
-            safety_settings=safety_settings
-        )
-
-        # Process response
-        output_path = os.path.join(temp_dir, "result.png")
-        for chunk in response:
-            if chunk.candidates:
-                for part in chunk.candidates[0].content.parts:
-                    if hasattr(part, 'inline_data'):
-                        with open(output_path, "wb") as f:
-                            f.write(part.inline_data.data)
-                        return jsonify({
-                            "success": True,
-                            "resultPath": output_path
-                        })
-
-        return jsonify({"success": False, "message": "No valid image data found in response"})
+            return jsonify({"success": False, "message": "Missing required parameters"})
+
+        # Decode image
+        try:
+            header, encoded = image_data.split(",", 1)
+            image_bytes = base64.b64decode(encoded)
+        except Exception as e:
+            logging.error(f"Image decoding failed: {str(e)}")
+            return jsonify({"success": False, "message": "Invalid image data"})
+
+        # Temporary files
+        with tempfile.TemporaryDirectory() as temp_dir:
+            input_path = os.path.join(temp_dir, "input.png")
+            with open(input_path, "wb") as f:
+                f.write(image_bytes)
+
+            # Configure model with safety settings
+            model = genai.GenerativeModel('gemini-2.0-flash-exp-image-generation')
+            prompt = f"Remove {object_type} from image naturally without text or artifacts"
+            
+            response = model.generate_content(
+                [prompt, genai.upload_file(input_path)],
+                generation_config={
+                    "temperature": 0.9,
+                    "top_p": 0.95,
+                    "top_k": 32,
+                    "max_output_tokens": 4096,
+                },
+                safety_settings={
+                    "HARM_CATEGORY_CIVIC_INTEGRITY": "BLOCK_ONLY_HIGH",
+                    "HARM_CATEGORY_HARASSMENT": "BLOCK_NONE",
+                    "HARM_CATEGORY_HATE_SPEECH": "BLOCK_NONE",
+                    "HARM_CATEGORY_SEXUALLY_EXPLICIT": "BLOCK_NONE",
+                    "HARM_CATEGORY_DANGEROUS_CONTENT": "BLOCK_NONE"
+                }
+            )
+
+            # Process response
+            output_path = os.path.join(temp_dir, "result.png")
+            for chunk in response:
+                if chunk.candidates:
+                    for part in chunk.candidates[0].content.parts:
+                        if hasattr(part, 'inline_data'):
+                            with open(output_path, "wb") as f:
+                                f.write(part.inline_data.data)
+                            return jsonify({
+                                "success": True,
+                                "resultPath": output_path
+                            })
+                        elif hasattr(part, 'text'):
+                            logging.info(f"Text response: {part.text}")
+
+            return jsonify({"success": False, "message": "No valid output generated"})
 
     except Exception as e:
-        return jsonify({"success": False, "message": str(e)})
+        logging.error(f"Processing error: {str(e)}")
+        return jsonify({"success": False, "message": f"Processing error: {str(e)}"})
 
 if __name__ == "__main__":
-    app.run(host="0.0.0.0", port=7860, debug=True)
+    app.run(host="0.0.0.0", port=5000, debug=False)