Create app.py

app.py

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+import gradio as gr
+import pickle
+
+# Load model
+with open("svm_model.pkl", "rb") as f:
+    model = pickle.load(f)
+
+# Prediction function
+def predict(sex, pregnant, TT4, T3, T4U, FTI, TSH):
+    try:
+        # Convert inputs to appropriate types
+        sex = int(sex)
+        pregnant = int(pregnant)
+        TT4 = float(TT4)
+        T3 = float(T3)
+        T4U = float(T4U)
+        FTI = float(FTI)
+        TSH = float(TSH)
+        prediction = model.predict([[sex, pregnant, TT4, T3, T4U, FTI, TSH]])
+        label_map = {0: "Hyperthyroid", 1: "Hypothyroid", 2: "Negative"}
+        return f"Prediction: {label_map.get(prediction[0], 'Unknown')}"
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Gradio UI
+demo = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Radio([0, 1], label="Sex (0: Female, 1: Male)"),
+        gr.Radio([0, 1], label="Pregnant (0: No, 1: Yes)"),
+        gr.Number(label="TT4"),
+        gr.Number(label="T3"),
+        gr.Number(label="T4U"),
+        gr.Number(label="FTI"),
+        gr.Number(label="TSH"),
+    ],
+    outputs="text",
+    title="Hyperthyroid Prediction",
+    description="Enter patient info to predict thyroid condition using SVM model."
+)
+
+if __name__ == "__main__":
+    demo.launch()