Merge branch 'main' of https://huggingface.co/spaces/Pranjal-666/Potato-leaf-disease-detection

app.py

@@ -4,7 +4,7 @@ import gradio as gr
 from PIL import Image
 from io import BytesIO
 import matplotlib.pyplot as plt
-
+import os 
 
 # Load the trained TensorFlow model using the Keras API
 model = tf.keras.models.load_model("potatoesV3.h5")
@@ -32,24 +32,36 @@ def classify_image(file):
  confidence = float(np.max(pred[0]))
 
  # Return the predicted class and confidence score
- 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]}
- 
+ 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]
 
 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')]