Update app.py

app.py

@@ -1,4 +1,4 @@
-import gradio as gr
+'''import gradio as gr
 import pickle
 import pandas as pd
 
@@ -35,3 +35,35 @@ iface = gr.Interface(
 
 # Launch the Gradio interface
 iface.launch()
+'''
+import gradio as gr
+import pickle
+import numpy as np
+
+# Load the trained model
+with open("model-svm.pkl", "rb") as f:
+    model = pickle.load(f)
+
+def predict_pcos(age, weight, height, bmi, pulse_rate, cycle_length):
+    input_data = np.array([[age, weight, height, bmi, pulse_rate, cycle_length]])
+    prediction = model.predict(input_data)
+    return "Positive" if prediction[0] == 1 else "Negative"
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=predict_pcos,
+    inputs=[
+        gr.Number(label="Age (yrs)"),
+        gr.Number(label="Weight (Kg)"),
+        gr.Number(label="Height (Cm)"),
+        gr.Number(label="BMI"),
+        gr.Number(label="Pulse rate(bpm)"),
+        gr.Number(label="Cycle length(days)")
+    ],
+    outputs=gr.Textbox(label="PCOS Prediction"),
+    title="PCOS Detection Model",
+    description="Predicts the likelihood of PCOS based on user input features."
+)
+
+# Launch the Gradio app
+interface.launch()