Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+
+model_path = r'./model.h5'
+
+# Function to preprocess the image and make predictions
+def classify_Lung_and_colon_image(input_image):
+    try:
+        # Preprocess the image (resize, normalize, etc.)
+        input_image = np.array(input_image)
+        input_image_copy = input_image.copy()  # Making a copy to avoid the array reference issue
+        input_image_resized = np.array(Image.fromarray(input_image_copy).resize((224, 224))) / 255.0
+        input_image_resized = np.expand_dims(input_image_resized, axis=0)
+
+        # Making predictions
+        model_h5 = tf.keras.models.load_model(model_path)
+        predictions = model_h5.predict(input_image_resized)
+
+        # Getting the class with the highest probability
+        class_idx = np.argmax(predictions)
+        class_label = ["Colon Adenocarcinoma", "Colon Benign Tissue", "Lung Adenocarcinoma", "Lung Benign Tissue", "Lung Squamous Cell Carcinoma"][class_idx]
+        confidence = predictions[0][class_idx]
+
+        return f"Prediction: {class_label}, Confidence: {confidence:.2f}"
+    except Exception as e:
+        return str(e)
+
+# Creating a Gradio interface
+iface = gr.Interface(
+    fn=classify_alzheimers_image,
+    inputs="image",
+    outputs="text",
+    title="Lung and Colon Cancer Detection",
+    description=" Upload a Histopathology Image for classification.",
+    flagging_options = ["Wrong Prediction"],
+    theme = 'darkhuggingface'
+)
+
+# Launching the Gradio interface
+iface.launch(inline = False)