Create app.py

app.py

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+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
+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)
+    ]
+    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
+def get_health_documents_from_groc(query):
+    api_key = "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"  # Replace with your GROC API key
+    url = f"https://api.groc.com/v1/search"
+    params = {
+        "query": query,
+        "api_key": api_key,
+        "type": "article"
+    }
+    response = requests.get(url, params=params)
+    if response.status_code == 200:
+        data = response.json()
+        return data.get("results", [])
+    else:
+        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
+def main():
+    st.title("Health Advisory Assistant")
+    st.write("A personalized health advisor based on student health data.")
+
+    # Sidebar for dataset upload
+    uploaded_file = st.sidebar.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")
+        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)
+
+        # 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"):
+            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.")
+
+            # 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)
+
+# Run the app
+if __name__ == "__main__":
+    main()