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import os
import streamlit as st
import pandas as pd
import requests
# Load the scholarships data
@st.cache_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() |