"request"

app.py

@@ -17,22 +17,23 @@ CLASS_NAMES = ["Early Blight", "Late Blight", "Healthy"]
 
 def classify_image(request):
  # Open the file using requests
- response = requests.get("file")
- img = Image.open(BytesIO(response.content))
+ image = request.files["file"]
 
- # 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)
+ image = np.array(
+ Image.open(image).convert("RGB").resize((256, 256)) # image resizing
+ )
 
- # Make predictions
- pred = model.predict(img_array)
+ image = image/255 # normalize the image in 0 to 1 range
 
- predicted_class = CLASS_NAMES[np.argmax(pred[0])]
- confidence = float(np.max(pred[0]))
+ img_array = tf.expand_dims(img, 0)
+ predictions = model.predict(img_array)
 
- # Return the predicted class and confidence score
- return {"class": predicted_class, "confidence": confidence,"predict":pred[0]}
+ 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}
  # Resize the image to the desired size
  # new_size = (256, 256)
  # img_resized = image.resize(new_size)