Update app.py

app.py

@@ -8,10 +8,6 @@ from PIL import Image
 import requests
 import os
 from io import BytesIO
-import logging
-
-# Set up basic logging
-logging.basicConfig(level=logging.INFO)
 
 # Define the number of classes
 num_classes = 2
@@ -41,9 +37,6 @@ transform = transforms.Compose([
     transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
 ])
 
-# Global variable to store the file path
-file_path = None
-
 # Function to predict from image content
 def predict_from_image(image):
     # Ensure the image is a PIL Image
@@ -66,46 +59,16 @@ def predict_from_image(image):
     else:
         return {"error": "Unexpected class prediction."}
 
-# Function to handle the file path sent via POST request
-def process_file_path(file_path_input):
-    global file_path
-    file_path = file_path_input  # Store the file path
-    
-    logging.info(f"Received file path: {file_path}")
-
-    # Ensure the path is correctly formatted for Windows
-    file_path = file_path.replace("\\", "/")  # Replace backslashes with forward slashes
-    logging.info(f"Corrected file path: {file_path}")
-    
-    if not os.path.exists(file_path):
-        logging.error(f"File not found at {file_path}")
-        return {"error": f"File not found at {file_path}"}
-    
-    image = Image.open(file_path)
-    logging.info(f"Processing image from path: {file_path}")
-    return predict_from_image(image)
-
-
-# Function to fetch the result (for the GET request)
-def fetch_result():
-    if file_path:
-        image = Image.open(file_path)
-        logging.info(f"Making prediction for image at path: {file_path}")
-        return predict_from_image(image)
-    else:
-        logging.warning("No file path available. Please send a POST request with a file path first.")
-        return {"error": "No file path available. Please send a POST request with a file path first."}
-
 # Gradio interface
 iface = gr.Interface(
-    fn=process_file_path,
+    fn=lambda image: predict_from_image(image)
     inputs=[
-        gr.Textbox(label="Enter Local Image Path", placeholder="Provide the local image path"),
+        gr.Image(type="pil", label="Upload an Image"),
     ],
     outputs=gr.JSON(label="Prediction Result"),
-    live=False,
+    live=True,
     title="Maize Anomaly Detection",
-    description="Provide a local file path via POST request to process an image.",
+    description="Upload an image or provide a URL to detect anomalies in maize crops.",
 )
 
 # Launch the interface