"ko:

app.py

@@ -15,25 +15,40 @@ CLASS_NAMES = ["Early Blight", "Late Blight", "Healthy"]
 
 # Define the function for making predictions
 
-def classify_image(request):
- # Open the file using requests
- image = request.files["file"]
-
- image = np.array(
- Image.open(image).convert("RGB").resize((256, 256)) # image resizing
- )
-
- image = image/255 # normalize the image in 0 to 1 range
-
- img_array = tf.expand_dims(img, 0)
- predictions = model.predict(img_array)
-
- print("Predictions:",predictions)
-
- predicted_class = class_names[np.argmax(predictions[0])]
- confidence = round(100 * (np.max(predictions[0])), 2)
-
- return {"class": predicted_class, "confidence": confidence}
+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]}
+#New updated def
+# 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)
@@ -67,7 +82,7 @@ def classify_image(request):
 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.File(label="Upload an image file")
+input_interface = gr.inputs.Image()
 
 # Define the Gradio interface for displaying the predicted class and confidence score
 output_interface = gr.Textbox()