fixed version

app.py

@@ -1,7 +1,6 @@
 import gradio as gr
 import joblib
 import pandas as pd
-import numpy as np
 
 # Load the pre-trained model
 model = joblib.load("tuned_model.pkl")
@@ -10,10 +9,13 @@ model = joblib.load("tuned_model.pkl")
 features = pd.read_csv("features_used_in_model.csv")["Feature"].tolist()
 
 # Prediction function
-def predict_heart_failure(input_data):
+def predict_heart_failure(*input_values):
     try:
-        # Convert input into a DataFrame
-        input_df = pd.DataFrame([input_data], columns=features)
+        # Convert inputs into a dictionary
+        input_data = dict(zip(features, input_values))
+        
+        # Convert input dictionary to DataFrame
+        input_df = pd.DataFrame([input_data])
         
         # Predict probability for heart failure (class 1)
         probability = model.predict_proba(input_df)[:, 1][0]
@@ -22,25 +24,21 @@ def predict_heart_failure(input_data):
         prediction = "At Risk of Heart Failure" if probability >= 0.3 else "No Risk Detected"
         
         # Return prediction, probability, and user inputs
-        return {
-            "Prediction": prediction,
-            "Risk Probability": round(probability, 4),
-            "User Inputs": input_data
-        }
+        return prediction, round(probability, 4), input_data
     except Exception as e:
-        return {"error": str(e)}
+        return "Error", 0, {"error": str(e)}
 
 # Gradio Interface
-inputs = []
-for feature in features:
-    inputs.append(gr.inputs.Textbox(label=feature, placeholder=f"Enter value for {feature}"))
-
-output = gr.outputs.JSON(label="Heart Failure Prediction & Inputs")
+inputs = [gr.Textbox(label=feature, placeholder=f"Enter value for {feature}") for feature in features]
 
 interface = gr.Interface(
     fn=predict_heart_failure,
     inputs=inputs,
-    outputs=output,
+    outputs=[
+        gr.Text(label="Prediction"),
+        gr.Number(label="Risk Probability"),
+        gr.JSON(label="User Inputs")
+    ],
     title="Heart Failure Prediction Model",
     description=(
         "Predicts the likelihood of heart failure based on health features. "
@@ -48,6 +46,4 @@ interface = gr.Interface(
     )
 )
 
-# Launch the interface for local testing or Hugging Face Spaces deployment
-if __name__ == "__main__":
-    interface.launch()
+# Launch the interface for local testing or

trained_model.pkl

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:671e17b4726b0253e73d9a3b6af013302cdd2c32086e9b67357d44f145af01cd
-size 7804185