"Update file upload"

app.py

@@ -14,55 +14,47 @@ CLASS_NAMES = ["Early Blight", "Late Blight", "Healthy"]
 
 
 # Define the function for making predictions
-def classify_image(image):
- # Open the image using Pillow
- img = Image.fromarray(image.astype('uint8'), 'RGB')
- new_size = (256, 256)
- img_resized = img.resize(new_size)
- #img_array = np.array(img_resized)
- #img_scaled = img_array / 255.0
- img_array = np.expand_dims(img_resized, axis=0)
+
+def classify_image(file):
+ # Open the file using requests
+ response = requests.get(file)
+ img = Image.open(BytesIO(response.content))
+
+ # Resize and preprocess the image
+ img_resized = img.convert("RGB").resize((256, 256))
+ img_array = np.array(img_resized) / 255.0
+ img_array = np.expand_dims(img_array, axis=0)
+
+ # Make predictions
  pred = model.predict(img_array)
 
  predicted_class = CLASS_NAMES[np.argmax(pred[0])]
  confidence = float(np.max(pred[0]))
 
  # Return the predicted class and confidence score
- return {"class": predicted_class, "confidence": confidence,"predict":pred[0]}
- # Resize the image to the desired size
- # new_size = (256, 256)
- # img_resized = image.resize(new_size)
- 
- # # Convert the image to a numpy array
- # img_array = np.array(img_resized)
- 
- # # Rescale the image pixel values to [0, 1]
- # img_scaled = img_array / 255.0
-
- # #img_array = np.array(img_scaled)
- # img_array = np.expand_dims(img_scaled, axis=0)
- # pred = model.predict(img_array)
- 
- # # Display the rescaled image
- # #plt.imshow(img_array)
- 
- # # Format the predicted class and confidence score
- # predicted_class = CLASS_NAMES[np.argmax(pred[0])]
- # confidence = float(np.max(pred[0]))
-
- # # Return the predicted class and confidence score
- # return {"class": predicted_class, "confidence": confidence,"predict":pred[0]}
- # img = (BytesIO(image))
- # img = img.resize((256, 256))
- # img_array = np.array(img)
- # img_array = np.expand_dims(img_array, axis=0)
- # pred = model.predict(img_array)
- # return pred[0][0]
+ return {"class": predicted_class, "confidence": confidence, "predict": pred[0]}
+
+# def classify_image(image):
+# # Open the image using Pillow
+# img = Image.fromarray(image.astype('uint8'), 'RGB')
+# new_size = (256, 256)
+# img_resized = img.resize(new_size)
+# #img_array = np.array(img_resized)
+# #img_scaled = img_array / 255.0
+# img_array = np.expand_dims(img_resized, axis=0)
+# pred = model.predict(img_array)
+
+# predicted_class = CLASS_NAMES[np.argmax(pred[0])]
+# confidence = float(np.max(pred[0]))
+
+# # Return the predicted class and confidence score
+# return {"class": predicted_class, "confidence": confidence,"predict":pred[0]}
+ 
 
 examples=[os.path.join(os.path.dirname(__file__),'7227b3db-c212-4370-8b42-443eea1577aa___RS_Early.B 7306.JPG','7456db33-766c-4a68-b924-ddf69d579981___RS_Early.B 6723.JPG','7486e823-64f7-4e43-ab51-26261b077fc2___RS_Early.B 6785.JPG','8829e413-5a7a-4680-b873-e71dfa9dbfe4___RS_LB 3974.JPG','9001b18c-b659-4c56-9dfb-0d0bf64a7b4a___RS_LB 4987.JPG','9009c86e-1205-4694-b0bb-ef7cf78dd104___RS_LB 3995.JPG','Potato_healthy-76-_0_2420.jpg','Potato_healthy-76-_0_6833.jpg','Potato_healthy-76-_0_7539.jpg')]
 
 # Define the Gradio interface for image input
-input_interface = gr.inputs.Image()
+input_interface = gr.inputs.File(label="Upload an image file")
 
 # Define the Gradio interface for displaying the predicted class and confidence score
 output_interface = gr.Textbox()