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import streamlit as st | |
from resume_parser import extract_resume_data | |
from gemini_api import authenticate_gemini, generate_summary | |
# Your hardcoded API key (You can also set this in environment variables) | |
api_key = "AIzaSyCG-qpFRqJc0QOJT-AcAaO5XIEdE-nk3Tc" | |
def main(): | |
st.title("Resume Analyzer") | |
st.write("Upload a resume to extract information") | |
uploaded_file = st.file_uploader("Choose a PDF or DOCX file", type=["pdf", "docx", "doc"]) | |
if uploaded_file is not None: | |
try: | |
# Authenticate with Google Gemini API | |
model = authenticate_gemini(api_key) | |
if model is None: | |
return | |
# Extract resume data | |
extracted_data, resume_text = extract_resume_data(uploaded_file) | |
# Generate summary using Gemini API | |
summary = generate_summary(resume_text, model) | |
# Display results | |
st.subheader("Extracted Information") | |
for key, value in extracted_data.items(): | |
st.write(f"*{key}:* {value}") | |
st.subheader("Generated Summary") | |
st.write(summary) | |
except Exception as e: | |
st.error(f"Error during processing: {e}") | |
if __name__ == "__main__": | |
main() | |