app.py

import os
import streamlit as st
import pandas as pd
import google.generativeai as genai

# Configuration
os.environ['GOOGLE_API_KEY'] = os.getenv('GOOGLE_API_KEY', 'your_key_here')

# Load scholarships data
@st.cache_data
def load_scholarships():
    scholarships = pd.read_csv("scholarships_data.csv")
    scholarships.rename(columns=lambda x: x.strip(), inplace=True)  # Clean column names
    required_columns = ['Scholarship Name', 'Eligibility', 'Link']
    if not all(col in scholarships.columns for col in required_columns):
        st.error(f"Missing required columns in the CSV file: {required_columns}")
        st.stop()
    return scholarships

# Initialize Generative AI
def get_genai_client():
    try:
        genai.configure(api_key=os.environ['GOOGLE_API_KEY'])
        return genai.GenerativeModel('gemini-pro')
    except Exception as e:
        st.error(f"AI Initialization Error: {str(e)}")
        return None

# Recommendation engine
def recommend_scholarships(user_data, scholarships):
    matches = []
    for _, row in scholarships.iterrows():
        eligibility = row['Eligibility'].lower()
        if (
            (user_data['citizenship'] in eligibility) and
            (user_data['age'] <= parse_age(eligibility)) and
            (user_data['income'] <= parse_income(eligibility)) and
            (user_data['education'] in eligibility) and
            (user_data['category'] in eligibility)
        ):
            matches.append(row)
    return pd.DataFrame(matches)

# Helper functions for eligibility parsing
def parse_age(eligibility):
    if 'below 35' in eligibility:
        return 35
    return 100  # Default if no age restriction

def parse_income(eligibility):
    if '₹2,50,000' in eligibility:
        return 250000
    return float('inf')  # Default if no income restriction

# Streamlit app
def main():
    st.title("AI-Powered Scholarship Advisor")
    
    # User input form
    with st.form("scholarship_form"):
        st.header("Student Profile")
        citizenship = st.selectbox("Citizenship", ["India", "Other"]).lower()
        age = st.number_input("Age", 1, 100, 25)
        income = st.number_input("Annual Family Income (₹)", 0, 10000000, 500000)
        education = st.selectbox(
            "Education Level",
            ["Class 10", "Class 12", "Undergraduate", 
             "Postgraduate", "PhD"]
        ).lower()
        category = st.selectbox(
            "Category", 
            ["General", "OBC", "SC", "ST", "EWS", "Minority"]
        ).lower()

        submitted = st.form_submit_button("Find Scholarships")  # Form submission button

    # Process form submission
    if submitted:
        # Validate inputs
        if not os.environ.get('GOOGLE_API_KEY'):
            st.warning("Please set GOOGLE_API_KEY environment variable")
            return

        # Load data and generate recommendations
        scholarships = load_scholarships()
        user_data = {
            'citizenship': citizenship,
            'age': age,
            'income': income,
            'education': education,
            'category': category
        }
        results = recommend_scholarships(user_data, scholarships)

        # Display results
        if not results.empty:
            st.subheader(f"Found {len(results)} Matching Scholarships")
            for _, row in results.iterrows():
                st.markdown(f"""
                    **{row['Scholarship Name']}**  
                    **Eligibility:** {row['Eligibility']}  
                    [Apply Here]({row['Link']})
                    """)
                st.divider()
        else:
            st.warning("No scholarships found matching your criteria.")

        # Generate AI-powered advice
        model = get_genai_client()
        if model:
            prompt = f"""
                Act as an experienced education counselor. 
                For a {age}-year-old {citizenship} citizen with:
                - Annual income: ₹{income}
                - Education level: {education}
                - Category: {category}
                Provide personalized advice about these scholarships:
                {results['Scholarship Name'].tolist()}
                """
            response = model.generate_content(prompt)
            st.subheader("AI Advisor Recommendations")
            st.write(response.text)

if __name__ == "__main__":
    main()