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| import os | |
| import streamlit as st | |
| import pandas as pd | |
| import requests | |
| # Load the scholarships data | |
| def load_scholarships_data(): | |
| return pd.read_csv("scholarships_data.csv") | |
| # Function to filter scholarships based on user input | |
| def recommend_scholarships(data, user_details): | |
| filtered_scholarships = [] | |
| for _, row in data.iterrows(): | |
| eligibility = row["Eligibility"].lower() | |
| if ( | |
| (user_details["citizenship"] == "india" and "indian" in eligibility) or | |
| (user_details["age"] <= 35 and "below 35" in eligibility) or | |
| (user_details["income"] <= 250000 and "₹2,50,000" in eligibility) or | |
| (user_details["education_level"] in eligibility) or | |
| (user_details["category"] in eligibility) | |
| ): | |
| filtered_scholarships.append(row) | |
| return pd.DataFrame(filtered_scholarships) | |
| # Function to call Gemini API | |
| def query_gemini(prompt): | |
| gemini_api_key = os.getenv("GEMINI_API_KEY") | |
| if not gemini_api_key: | |
| st.error("Gemini API Key not found in environment variables.") | |
| return None | |
| url = "https://api.gemini.com/v1/query" | |
| headers = { | |
| "Authorization": f"Bearer {gemini_api_key}", | |
| "Content-Type": "application/json", | |
| } | |
| payload = {"prompt": prompt} | |
| response = requests.post(url, headers=headers, json=payload) | |
| if response.status_code == 200: | |
| return response.json().get("response", "") | |
| else: | |
| return "Error: Unable to fetch response from Gemini API." | |
| # Streamlit App | |
| def main(): | |
| st.title("Scholarship Recommendation System") | |
| # User Input Form | |
| st.header("Student Scholarship Application Form") | |
| with st.form("student_form"): | |
| st.subheader("Personal Details") | |
| citizenship = st.selectbox("Citizenship", ["India", "Other"]) | |
| age = st.number_input("Age", min_value=1, max_value=100) | |
| income = st.number_input("Annual Family Income (in ₹)", min_value=0, max_value=10000000) | |
| education_level = st.selectbox( | |
| "Education Level", | |
| ["Class 10", "Class 12", "Undergraduate", "Postgraduate", "PhD"] | |
| ) | |
| category = st.selectbox( | |
| "Category", | |
| ["General", "OBC", "SC", "ST", "EWS", "Minority"] | |
| ) | |
| submit_button = st.form_submit_button("Find Scholarships") | |
| # Process form submission | |
| if submit_button: | |
| # Load scholarships data | |
| scholarships_data = load_scholarships_data() | |
| # Prepare user details | |
| user_details = { | |
| "citizenship": citizenship.lower(), | |
| "age": age, | |
| "income": income, | |
| "education_level": education_level.lower(), | |
| "category": category.lower(), | |
| } | |
| # Filter scholarships | |
| recommended_scholarships = recommend_scholarships(scholarships_data, user_details) | |
| # Display results | |
| st.subheader("Recommended Scholarships") | |
| if not recommended_scholarships.empty: | |
| for _, scholarship in recommended_scholarships.iterrows(): | |
| st.markdown(f"**{scholarship['Scholarship Name']}**") | |
| st.write(f"**Eligibility:** {scholarship['Eligibility']}") | |
| st.write(f"**Link:** {scholarship['Link']}") | |
| st.write("---") | |
| else: | |
| st.warning("No scholarships found matching your criteria.") | |
| # Ask Gemini for additional advice | |
| prompt = ( | |
| f"Provide advice for a {age}-year-old {citizenship} citizen with an annual family income of ₹{income}, " | |
| f"pursuing {education_level}, belonging to the {category} category, to find suitable scholarships." | |
| ) | |
| gemini_response = query_gemini(prompt) | |
| if gemini_response: | |
| st.subheader("Additional Advice from Gemini") | |
| st.write(gemini_response) | |
| if __name__ == "__main__": | |
| main() |