Update app.py

app.py

@@ -4,6 +4,7 @@ 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"
@@ -12,6 +13,39 @@ 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',