"Main"

app.py

@@ -1,7 +1,69 @@
+import numpy as np
+import tensorflow as tf
 import gradio as gr
+from PIL import Image
+from io import BytesIO
+import matplotlib.pyplot as plt
 
-def greet(name):
- return "Hello " + name + "!!"
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+# Load the trained TensorFlow model using the Keras API
+model = tf.keras.models.load_model("potatoesV3.h5")
+
+# Define the class names for classification
+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)
+ 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]
+
+# Define the Gradio interface for image input
+input_interface = gr.inputs.Image()
+
+# Define the Gradio interface for displaying the predicted class and confidence score
+output_interface = gr.Textbox()
+
+# Launch the Gradio interface with the classify_image function as the prediction function
+gr.Interface(fn=classify_image, inputs=input_interface, outputs=output_interface).launch()

potatoesV3.h5

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b00b98de869b8adcb804800e0f5277fc078963668a6b7ff50e8718bdbdfea531
+size 2282408

requirements.txt

@@ -0,0 +1,5 @@
+numpy
+tensorflow
+gradio
+pillow
+matplotlib