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 TFLite model
+interpreter = tf.lite.Interpreter(model_path="stool_model.tflite")
+interpreter.allocate_tensors()
+
+input_details = interpreter.get_input_details()
+output_details = interpreter.get_output_details()
+
+# Define label names (adjust if your model uses different ones)
+labels = ["bloody", "hard stool", "normal", "parasite", "watery"]
+
+def preprocess_image(img: Image.Image):
+    img = img.convert("RGB").resize((128, 128))
+    arr = np.asarray(img).astype(np.float32) / 255.0
+    arr = np.expand_dims(arr, axis=0)
+    return arr
+
+def classify_image(image):
+    try:
+        input_data = preprocess_image(image)
+        interpreter.set_tensor(input_details[0]['index'], input_data)
+        interpreter.invoke()
+        output_data = interpreter.get_tensor(output_details[0]['index'])[0]
+        results = {labels[i]: float(output_data[i]) for i in range(len(labels))}
+        sorted_results = dict(sorted(results.items(), key=lambda x: x[1], reverse=True))
+        return sorted_results
+    except Exception as e:
+        return {"error": str(e)}
+
+demo = gr.Interface(
+    fn=classify_image,
+    inputs=gr.Image(type="pil", label="Upload stool image"),
+    outputs=gr.Label(num_top_classes=3, label="Predictions"),
+    title="Stool Diagnosis Model",
+    description="Upload a stool image for classification."
+)
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", server_port=7860)