Update app.py

app.py

@@ -1,29 +1,22 @@
 import streamlit as st
 import pandas as pd
 import requests
-from transformers import pipeline
-import datetime
 
-# Load the dataset
-@st.cache
-def load_data(file):
-    return pd.read_csv(file)
-
-# Fetch health advice from the dataset
+# Function to get health advice based on inputs
 def get_health_advice(df, age, heart_rate, systolic_bp, diastolic_bp):
     filtered_df = df[
-        (df['Age'] == age) &
-        (df['Heart_Rate'] == heart_rate) &
-        (df['Blood_Pressure_Systolic'] == systolic_bp) &
-        (df['Blood_Pressure_Diastolic'] == diastolic_bp)
+        (df['Age'].between(age - 2, age + 2)) &  # Allow ±2 years
+        (df['Heart_Rate'].between(heart_rate - 5, heart_rate + 5)) &  # Allow ±5 bpm
+        (df['Blood_Pressure_Systolic'].between(systolic_bp - 10, systolic_bp + 10)) &  # Allow ±10
+        (df['Blood_Pressure_Diastolic'].between(diastolic_bp - 10, diastolic_bp + 10))  # Allow ±10
     ]
     if not filtered_df.empty:
         return filtered_df.iloc[0]['Health_Risk_Level']
     return "No matching health data found."
 
-# Fetch related articles using the GROC API
+# Function to get health articles from GROC API
 def get_health_documents_from_groc(query):
-    api_key = "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"  # Replace with your GROC API key
+    api_key = "YOUR_GROC_API_KEY"  # Replace with your actual GROC API key
     url = f"https://api.groc.com/v1/search"
     params = {
         "query": query,
@@ -35,57 +28,37 @@ def get_health_documents_from_groc(query):
         data = response.json()
         return data.get("results", [])
     else:
+        st.error(f"Error {response.status_code}: {response.text}")
         return [{"title": f"Error: {response.status_code}", "url": ""}]
 
-# GPT-2 Model for generating advice
-@st.cache(allow_output_mutation=True)
-def load_gpt2_model():
-    return pipeline("text-generation", model="gpt2")
-
-# Main Streamlit App
+# Main Streamlit app
 def main():
-    st.title("Health Advisory Assistant")
-    st.write("A personalized health advisor based on student health data.")
+    st.title("Health Risk Level and Advisory Assistant")
 
-    # Sidebar for dataset upload
-    uploaded_file = st.sidebar.file_uploader("Upload your dataset (CSV)", type=["csv"])
+    # File upload
+    uploaded_file = st.file_uploader("Upload your dataset (CSV)", type="csv")
     if uploaded_file is not None:
-        df = load_data(uploaded_file)
-        st.sidebar.success("Dataset loaded successfully!")
-        st.write("### Dataset Preview")
+        df = pd.read_csv(uploaded_file)
+        st.write("Dataset Preview:")
         st.dataframe(df.head())
 
-        # User input for health parameters
-        st.write("### Input Health Parameters")
-        age = st.number_input("Age", min_value=0, max_value=100, value=25)
-        heart_rate = st.number_input("Heart Rate (bpm)", min_value=0, max_value=200, value=72)
-        systolic_bp = st.number_input("Systolic Blood Pressure", min_value=0, max_value=200, value=120)
-        diastolic_bp = st.number_input("Diastolic Blood Pressure", min_value=0, max_value=200, value=80)
+        # User input
+        age = st.number_input("Enter Age", min_value=1, max_value=100, step=1)
+        heart_rate = st.number_input("Enter Heart Rate (bpm)", min_value=30, max_value=200, step=1)
+        systolic_bp = st.number_input("Enter Systolic Blood Pressure", min_value=80, max_value=200, step=1)
+        diastolic_bp = st.number_input("Enter Diastolic Blood Pressure", min_value=40, max_value=120, step=1)
 
-        # Severity slider
-        severity = st.slider("Severity (1-10)", min_value=1, max_value=10, value=5)
-
-        # Fetch health advice
-        if st.button("Get Health Advice"):
+        # Predict health risk level
+        if st.button("Get Health Risk Level"):
             risk_level = get_health_advice(df, age, heart_rate, systolic_bp, diastolic_bp)
-            st.write(f"**Health Risk Level**: {risk_level}")
-
-            # Fetch related health articles
-            st.write("### Related Health Articles")
-            articles = get_health_documents_from_groc("Blood Pressure and Heart Rate")
-            if articles:
-                for article in articles:
-                    st.write(f"- [{article['title']}]({article['url']})")
-            else:
-                st.write("No articles found.")
+            st.write(f"Health Risk Level: {risk_level}")
 
-            # Generate GPT-2 response
-            gpt2_model = load_gpt2_model()
-            advice_prompt = f"Provide health advice for a person with Age: {age}, Heart Rate: {heart_rate}, Systolic BP: {systolic_bp}, Diastolic BP: {diastolic_bp}, and Severity: {severity}."
-            response = gpt2_model(advice_prompt, max_length=100)[0]['generated_text']
-            st.write("### AI-Generated Advice")
-            st.write(response)
+            # Retrieve related health articles
+            query = f"Health risk for age {age}, heart rate {heart_rate}, BP {systolic_bp}/{diastolic_bp}"
+            st.write("Related Health Articles:")
+            articles = get_health_documents_from_groc(query)
+            for article in articles:
+                st.markdown(f"- [{article['title']}]({article['url']})")
 
-# Run the app
 if __name__ == "__main__":
     main()