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| import streamlit as st | |
| import google.generativeai as genai | |
| from dotenv import load_dotenv | |
| import os | |
| import json | |
| # Load environment variables | |
| load_dotenv() | |
| # Configure Gemini | |
| genai.configure(api_key=os.getenv("GEMINI_API_KEY")) | |
| model = genai.GenerativeModel('gemini-pro') | |
| # Load questionnaire from JSON | |
| def load_questionnaire(): | |
| with open('assets/questionnaire.json', 'r') as f: | |
| return json.load(f) | |
| # Render questions based on type | |
| def render_question(question): | |
| question_type = question.get("type") | |
| if question_type == "slider": | |
| return st.slider( | |
| question["question"], | |
| min_value=question.get("min", 1), | |
| max_value=question.get("max", 10), | |
| value=question.get("default", 5) | |
| ) | |
| elif question_type == "select_slider": | |
| return st.select_slider( | |
| question["question"], | |
| options=question["options"], | |
| value=question.get("default") | |
| ) | |
| elif question_type == "radio": | |
| return st.radio( | |
| question["question"], | |
| options=question["options"] | |
| ) | |
| elif question_type == "select": | |
| return st.selectbox( | |
| question["question"], | |
| options=question["options"] | |
| ) | |
| elif question_type == "multiselect": | |
| return st.multiselect( | |
| question["question"], | |
| options=question["options"] | |
| ) | |
| elif question_type == "number": | |
| return st.number_input( | |
| question["question"], | |
| min_value=question.get("min", 0), | |
| max_value=question.get("max", 24), | |
| value=question.get("default", 4) | |
| ) | |
| else: | |
| st.warning(f"Unsupported question type: {question_type}") | |
| return None | |
| # Main function | |
| def main(): | |
| st.title("JEE SOCA Analysis System 🚀") | |
| st.subheader("AI-Powered Skill Assessment for JEE Aspirants") | |
| # Load questionnaire | |
| questionnaire = load_questionnaire() | |
| # Collect responses | |
| responses = {} | |
| with st.form("student_form"): | |
| st.header("Student Questionnaire") | |
| # Render questions from JSON | |
| for section in questionnaire["questionnaire"]: | |
| st.subheader(f"📚 {section['section']}") | |
| for question in section["questions"]: | |
| response = render_question(question) | |
| if response is not None: | |
| responses[question["question"]] = response | |
| # Submit button | |
| submitted = st.form_submit_button("Generate SOCA Analysis") | |
| if submitted: | |
| with st.spinner("Analyzing responses..."): | |
| # Prepare prompt for Gemini | |
| prompt = "Analyze this JEE student's profile and create a SOCA analysis:\n\n" | |
| for question, response in responses.items(): | |
| prompt += f"- {question}: {response}\n" | |
| prompt += """ | |
| Provide the analysis in this format: | |
| **Strengths:** [Identify 3 key strengths] | |
| **Opportunities:** [Suggest 3 improvement areas] | |
| **Challenges:** [List 3 main challenges] | |
| **Action Plan:** [Create 4 actionable steps] | |
| """ | |
| # Get Gemini response | |
| try: | |
| response = model.generate_content(prompt) | |
| st.subheader("SOCA Analysis Report") | |
| st.markdown(response.text) | |
| except Exception as e: | |
| st.error(f"An error occurred while generating the analysis: {e}") | |
| # Run the app | |
| if __name__ == "__main__": | |
| main() |