Create app.py

app.py

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+import gradio as gr
+import numpy as np
+import tensorflow as tf
+
+# Load your model
+model = tf.keras.models.load_model("model.h5")  # Ensure this is the correct path to your model
+
+def predict(image):
+    # Preprocess the image for prediction
+    image = tf.image.resize(image, [224, 224])  # Change to your model's expected size
+    image = np.expand_dims(image, axis=0)  # Add batch dimension
+    predictions = model.predict(image)  # Get model predictions
+    
+    # Assuming your model outputs probabilities for binary classification
+    # The first output is the probability of class 0 (no tumor), 
+    # and the second output is the probability of class 1 (tumor)
+    no_tumor_confidence = predictions[0][0]  # Probability of no tumor
+    tumor_confidence = predictions[0][1]      # Probability of tumor
+
+    # Create a response with confidence scores
+    if tumor_confidence > no_tumor_confidence:
+        result = {
+            "prediction": "Tumor Detected",
+            "confidence": float(tumor_confidence)
+        }
+    else:
+        result = {
+            "prediction": "No Tumor Detected",
+            "confidence": float(no_tumor_confidence)
+        }
+
+    return result
+
+# Create a Gradio interface
+iface = gr.Interface(fn=predict, inputs="image", outputs="json")
+
+# Launch the Gradio interface
+iface.launch()