Create app.py

app.py

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+import gradio as gr
+import numpy as np
+from PIL import Image
+import tensorflow as tf
+
+# Load TFLite model
+interpreter = tf.lite.Interpreter(model_path="model.tflite")
+interpreter.allocate_tensors()
+
+# Get input and output details
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+class_names = ['Dyskeratotic', 'Koilocytotic', 'Metaplastic', 'Parabasal', 'Superficial-Intermediat']
+CONFIDENCE_THRESHOLD = 0.7
+
+def predict_image(image):
+    try:
+        image = image.resize((224, 224)).convert("RGB")
+        img_array = np.array(image, dtype=np.float32) / 255.0
+        img_array = np.expand_dims(img_array, axis=0)
+
+        interpreter.set_tensor(input_details[0]['index'], img_array)
+        interpreter.invoke()
+        output = interpreter.get_tensor(output_details[0]['index'])
+
+        class_idx = int(np.argmax(output))
+        confidence = float(np.max(output))
+
+        if confidence < CONFIDENCE_THRESHOLD:
+            return f"⚠️ Low confidence ({confidence:.2f}). The model is unsure. Please try a clearer image."
+        else:
+            return f"✅ Prediction: {class_names[class_idx]} (Confidence: {confidence:.2f})"
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+gr.Interface(
+    fn=predict_image,
+    inputs=gr.Image(type="pil"),
+    outputs="text",
+    title="Cervical Cancer Classification",
+    description="Upload an image to detect cervical cancer."
+).launch()