Create app.py

app.py

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+import gradio as gr
+import tensorflow as tf
+import numpy as np
+from PIL import Image
+
+# Load the trained model
+model = tf.keras.models.load_model("cancer_classifier.h5")
+
+# Define the class names (make sure they match your dataset)
+class_names = ['Basophil', 'Eosinophil', 'Erythroblast', 'IG', 'lymphocyte', 'Monocyte', 'Neutrophil', 'Platelet']
+
+# Define prediction function
+def predict(image):
+    image = image.resize((224, 224))
+    img_array = np.array(image) / 255.0
+    img_array = np.expand_dims(img_array, axis=0)
+
+    prediction = model.predict(img_array)
+    class_index = np.argmax(prediction)
+    confidence = float(np.max(prediction))
+
+    return {
+        "Predicted Class": class_names[class_index],
+        "Confidence": round(confidence * 100, 2)
+    }
+
+# Gradio interface
+iface = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil", label="Upload Blood Cell Image"),
+    outputs=[
+        gr.Label(label="Prediction"),
+    ],
+    title="🧬 Blood Cell Cancer Classifier",
+    description="Upload a blood cell image to classify whether it's cancerous or not using a fine-tuned EfficientNetB3 model.",
+    theme="gradio/soft"
+)
+
+if __name__ == "__main__":
+    iface.launch()