Update app.py

app.py

@@ -2,55 +2,50 @@ import gradio as gr
 import numpy as np
 from tensorflow.keras.models import load_model
 from tensorflow.keras.preprocessing import image
+from PIL import Image
 
 # Load your custom model
 model = load_model("lung_cancer_detection_model.h5", compile=False)
 
-# Then, compile your model using the optimizer
+# Compile your model using the optimizer
 model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])
 
-
-    
 # Function to preprocess the uploaded image
-def predict_single_image(image_path, model, target_size=(128, 128)):
-    # Load and preprocess the image
-    img = image.load_img(image_path, target_size=target_size, color_mode="grayscale")
-    img = image.img_to_array(img)
-    img = np.expand_dims(img, axis=0)
-    img /= 255.0  # Rescale the image
+def predict_single_image(img, model, target_size=(128, 128)):
+    # Convert the PIL image to grayscale and resize
+    img = img.convert("L")  # Convert to grayscale
+    img = img.resize(target_size)  # Resize to target size
+
+    # Convert image to array
+    img_array = np.array(img)
+    img_array = np.expand_dims(img_array, axis=0)  # Expand dimensions for batch
+    img_array = img_array / 255.0  # Normalize the image
 
     # Predict the class probabilities
-    probabilities = model.predict(img)
-    # display(probabilities)
+    probabilities = model.predict(img_array)
 
     # Determine the predicted class label
     predicted_class = "positive" if probabilities[0][0] > 0.5 else "negative"
 
     return predicted_class, probabilities[0][0]
 
-
 # Function to classify the uploaded image
 def classify_lung_cancer(img):
     # Call the function to predict the class label for the single image
-    predicted_label, confidence = predict_single_image(
-        img, model, target_size=(512, 512)
-    )
+    predicted_label, confidence = predict_single_image(img, model, target_size=(128, 128))
 
-    # Print the prediction
-    # print('Predicted Label:', predicted_label)
-    # print('Confidence:', confidence)
+    # Return the formatted prediction
     return f"Prediction: {predicted_label}\n(Confidence: {confidence:.2f})"
 
-
 # Define the Gradio interface
 iface = gr.Interface(
     fn=classify_lung_cancer,
-    inputs=gr.components.Image(type="filepath"),
-    outputs=gr.components.Textbox(),
+    inputs=gr.Image(type="pil"),  # Pass the PIL image directly
+    outputs=gr.Textbox(),
     live=True,
     title="Lung Cancer Classification",
     description="Upload an image and the model will classify it as positive or negative for lung cancer.",
 )
 
 # Create a Gradio interface
-iface.launch()
+iface.launch(share=True)