application code

model.py

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+#Code was Designed and Developed by 'SKAV TECH' Company
+import os
+import tensorflow as tf
+from tensorflow.keras.preprocessing.image import load_img, img_to_array
+import numpy as np
+import gradio as gr
+
+# Force TensorFlow to use CPU
+os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
+
+# Load the pre-trained model
+model = tf.keras.models.load_model("maheshbabu.h5")
+
+# Define class labels
+classes = ["Normal", "Cancerous"]
+
+# Prediction function
+def predict(images):
+    results = []
+    cancerous_count = 0
+
+    for image in images:
+        try:
+            # Load and preprocess the image
+            img = load_img(image, target_size=(224, 224))  # Resize image
+            img_array = img_to_array(img) / 255.0          # Normalize pixel values
+            img_array = np.expand_dims(img_array, axis=0)  # Add batch dimension
+
+            # Perform prediction
+            predictions = model.predict(img_array)
+            class_idx = np.argmax(predictions[0])          # Get index of highest probability
+            confidence = predictions[0][class_idx]        # Get confidence score
+
+            result = f"{classes[class_idx]} ({confidence:.2f})"
+            results.append(result)
+
+            if classes[class_idx] == "Cancerous":
+                cancerous_count += 1
+        except Exception as e:
+            results.append(f"Error processing image: {str(e)}")
+
+    # Generate final summary
+    if cancerous_count > 0:
+        summary = f"Warning: {cancerous_count} out of {len(images)} samples are Cancerous. Please consult a doctor."
+    else:
+        summary = "All samples are Normal. No signs of cancer detected."
+
+    return results, summary
+
+# Set up the Gradio interface
+interface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="filepath", label="Upload Blood Cell Images", tool=None, shape=None, source="upload", multiple=True),  # Allow multiple images
+    outputs=[
+        gr.JSON(label="Detailed Results"),
+        gr.Textbox(label="Final Summary")
+    ],
+    title="Enhanced Blood Cancer Detection",
+    description=(
+        "Upload 5-10 blood cell images to detect whether they are Normal or Cancerous. "
+        "The application uses a deep learning model to analyze each sample. "
+        "[Learn more about early cancer detection](https://www.cancer.org)."
+    ),
+    live=True,
+    theme="compact"
+)
+
+if __name__ == "__main__":
+    interface.launch(server_port=7860, server_name="0.0.0.0", share=True)