Create app.py

app.py

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+import gradio as gr
+import pickle
+import pandas as pd
+
+# Load the saved model
+with open("model_AD.pkl", "rb") as f:
+    model = pickle.load(f)
+
+def predict_pcos(input_features):
+    # Convert input features into a DataFrame (assuming 8 features here)
+    input_data = pd.DataFrame([input_features], columns=["Age (yrs)", "BMI", "Weight (Kg)", "Cycle length(days)", "Follicle No. (L)", "Follicle No. (R)", "AMH(ng/mL)", "beta-HCG(mIU/mL)"])
+
+    # Predict using the loaded model
+    prediction = model.predict(input_data)
+
+    return "PCOS Positive" if prediction[0] == 1 else "PCOS Negative"
+
+# Define Gradio inputs and outputs
+iface = gr.Interface(
+    fn=predict_pcos,
+    inputs=[
+        gr.inputs.Number(label="Age (yrs)"),
+        gr.inputs.Number(label="BMI"),
+        gr.inputs.Number(label="Weight (Kg)"),
+        gr.inputs.Number(label="Cycle length(days)"),
+        gr.inputs.Number(label="Follicle No. (L)"),
+        gr.inputs.Number(label="Follicle No. (R)"),
+        gr.inputs.Number(label="AMH(ng/mL)"),
+        gr.inputs.Number(label="beta-HCG(mIU/mL)")
+    ],
+    outputs="text",
+    title="PCOS Detection",
+    description="Predicts PCOS based on user-provided medical data."
+)
+
+# Launch the Gradio interface
+iface.launch()