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| import streamlit as st | |
| from src.resume_parser import ResumeParser | |
| from generate_questions.questions_generator import QuestionGenerator | |
| import google.generativeai as genai | |
| import os | |
| from src.greetings import end_conversation,start_greeting | |
| import json | |
| from src.feedback import feed_back | |
| genai.configure(api_key=os.getenv("GOOGLE_API_KEY")) | |
| model = genai.GenerativeModel("gemini-pro") | |
| chat = model.start_chat(history=[]) | |
| resume_parser = ResumeParser() | |
| questions_generator = QuestionGenerator() | |
| class ResumeUploader: | |
| def resume_upload(self): | |
| st.title("TalentScout - Hiring Assistant Chatbot") | |
| start_greeting() | |
| st.sidebar.title("Candidate Details") | |
| if "tech_stack" not in st.session_state: | |
| st.session_state.tech_stack = [] | |
| if "questions" not in st.session_state: | |
| st.session_state.questions = {} | |
| if "answers" not in st.session_state: | |
| st.session_state.answers = {} | |
| uploaded_file = st.sidebar.file_uploader("Upload Resume (PDF):", type=["pdf"]) | |
| if uploaded_file: | |
| candidate_details = resume_parser.parse(uploaded_file) | |
| st.success("Resume uploaded and processed successfully!") | |
| st.write("### Candidate Information") | |
| st.write(f"**Name:** {candidate_details['name']}") | |
| st.write(f"**Email:** {candidate_details['email']}") | |
| st.write(f"**Phone:** {candidate_details['phone']}") | |
| st.write(f"**Experience:** {candidate_details['experience']}") | |
| st.write(f"**Position:** {candidate_details['position']}") | |
| st.write(f"**Location:** {candidate_details['location']}") | |
| st.write(f"**Tech Stack:** {', '.join(candidate_details['tech_stack'])}") | |
| st.session_state.tech_stack = candidate_details["tech_stack"][:5] | |
| if st.session_state.tech_stack: | |
| st.write("**Based on your Top 5 skills ,we generate Technical Question , you need to answer the question .If you are ready please click Generate Questions button**") | |
| if st.button("Generate Questions"): | |
| for tech in st.session_state.tech_stack: | |
| tech = tech.strip() | |
| if tech not in st.session_state.questions: | |
| questions = questions_generator.generate_questions(chat, tech) | |
| st.session_state.questions[tech] = questions.split("\n") | |
| st.session_state.answers[tech] = [""] * len(st.session_state.questions[tech]) | |
| # Display questions and answers | |
| if st.session_state.questions: | |
| for tech, questions in st.session_state.questions.items(): | |
| st.write(f"### Technical Questions for {tech}") | |
| for idx, question in enumerate(questions): | |
| st.write(f"{idx + 1}. {question}") | |
| st.session_state.answers[tech][idx] = st.text_area( | |
| f"Answer for Q{idx + 1} ({tech})", | |
| value=st.session_state.answers[tech][idx], | |
| key=f"answer_{tech}_{idx}",) | |
| if st.button("Submit Answers"): | |
| # Combine questions and answers into a JSON format | |
| greets = end_conversation() | |
| st.write(greets) | |
| output_data = [] | |
| for tech, questions in st.session_state.questions.items(): | |
| for idx, question in enumerate(questions): | |
| output_data.append({ | |
| "tech": tech, | |
| "question": question, | |
| "answer": st.session_state.answers[tech][idx] | |
| }) | |
| # Save as JSON file | |
| json_filename = "questions_answers.json" | |
| with open(json_filename, "w") as json_file: | |
| json.dump(output_data, json_file, indent=4) | |
| feed_back() |