Create app.py

app.py

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+import streamlit as st
+import requests
+import os
+from dotenv import load_dotenv
+from transformers import pipeline
+
+# Load model directly using the pipeline
+pipe = pipeline("fill-mask", model="microsoft/deberta-v3-base")
+
+# Load environment variables
+load_dotenv()
+
+# Streamlit app configuration
+st.set_page_config(page_title="AI Healthcare Status Checker", page_icon="🧠")
+st.title("🧠 AI Healthcare Status Checker")
+
+# User input section
+with st.form("health_inputs"):
+    age = st.slider("Age", 1, 100, 25)
+    weight = st.number_input("Weight (kg)", min_value=1.0, value=70.0)
+    height = st.number_input("Height (cm)", min_value=50.0, value=170.0)
+    systolic = st.slider("Systolic BP", 80, 200, 120)
+    diastolic = st.slider("Diastolic BP", 50, 130, 80)
+    heart_rate = st.slider("Heart Rate (bpm)", 40, 200, 72)
+    submitted = st.form_submit_button("Check with AI")
+
+# Calculate BMI
+def calculate_bmi(height, weight):
+    height_m = height / 100
+    return weight / (height_m ** 2)
+
+# API configuration
+HF_TOKEN = os.getenv("HF_TOKEN")
+API_URL = "https://api-inference.huggingface.co/models/cardioai/risk-prediction-v1"
+headers = {"Authorization": f"Bearer {HF_TOKEN}"} if HF_TOKEN else {}
+
+def query_api(payload):
+    try:
+        response = requests.post(API_URL, headers=headers, json=payload, timeout=30)
+        response.raise_for_status()  # Raise an error for bad HTTP response codes
+        return response.json()
+    except requests.exceptions.RequestException as e:
+        st.error(f"API request failed: {str(e)}")
+        return None
+
+if submitted:
+    bmi = calculate_bmi(height, weight)
+    st.write(f"📊 Calculated BMI: {bmi:.2f}")
+    
+    # Prepare input data for the AI model
+    input_data = {
+        "inputs": {
+            "age": age,
+            "bmi": round(bmi, 2),
+            "systolic": systolic,
+            "diastolic": diastolic,
+            "heart_rate": heart_rate
+        }
+    }
+
+    with st.spinner("Analyzing your health data..."):
+        output = query_api(input_data)
+    
+    if output:
+        try:
+            label = output[0].get('label', '')
+            score = output[0].get('score', 0)
+            
+            if label == 'LABEL_0':
+                st.success(f"✅ You are Healthy! (Confidence: {score:.2f})")
+            else:
+                st.warning(f"⚠️ Health Needs Attention! (Confidence: {score:.2f})")
+                
+                # Additional health tips
+                if bmi > 25:
+                    st.info("💡 Your BMI suggests you might be overweight. Consider consulting a nutritionist.")
+                if systolic > 140 or diastolic > 90:
+                    st.info("💡 Your blood pressure is elevated. Regular monitoring is recommended.")
+                    
+        except (KeyError, IndexError) as e:
+            st.error(f"Error processing API response: {str(e)}")