Update app.py

app.py

@@ -3,8 +3,6 @@ import numpy as np
 import joblib
 from fastapi import FastAPI, HTTPException
 from pydantic import BaseModel
-import uvicorn
-import streamlit as st
 
 # Load model from the local storage (ensure the model file is in the same directory)
 model_path = "model.pkl"
@@ -13,39 +11,6 @@ gb_model_loaded = joblib.load(model_path)
 # Create FastAPI app
 app = FastAPI()
 
-# Set the title of the app
-st.title("Medical Prediction Model")
-
-# Instruction text
-st.write("Enter 32 features for prediction:")
-
-# Create 32 input fields for user input
-inputs = []
-for i in range(32):
-    value = st.number_input(f"Feature {i + 1}", min_value=0, step=1)
-    inputs.append(value)
-
-# Button to make prediction
-if st.button("Predict"):
-    # Prepare the data for the request
-    input_data = {"features": inputs}
-    
-    # Set the URL for your Hugging Face Space
-    url = "https://phoner45-mediguide-api.hf.space/predict"  # Replace with your actual Space URL
-
-    # Make a POST request
-    response = requests.post(url, json=inputs)
-    
-    # Check the response status code
-    if response.status_code == 200:
-        # Get the JSON response
-        prediction = response.json()
-        # Display the prediction results
-        st.success("Prediction Results:")
-        st.json(prediction)
-    else:
-        st.error(f"Error: {response.status_code} - {response.text}")
-        
 # Define class labels
 class_names = [
     'Emergency & Accident Unit', 'Heart Clinic',
@@ -82,6 +47,9 @@ def predict(data: InputData):
     except Exception as e:
         raise HTTPException(status_code=500, detail=str(e))
 
-# Run the application with the following command if needed
+# To run the FastAPI app locally for testing
+# Uncomment the following lines
 # if __name__ == "__main__":
+#     import uvicorn
 #     uvicorn.run(app, host="0.0.0.0", port=8501)
+