')
+ text = text.replace('\n', '
')
+
+ # Wrap in paragraph if not starting with a tag
+ if not text.startswith('<'):
+ text = f'
{text}
' + + return text + + +# Register Jinja filter +app.jinja_env.filters['format_markdown'] = format_markdown + + +@app.route('/') +def index(): + """Landing page with input form""" + return render_template('index.html') + + +@app.route('/about') +def about(): + """About page""" + return render_template('about.html') + + +@app.route('/health') +def health(): + """Health check endpoint for monitoring""" + from config import GROQ_API_KEY, TAVILY_API_KEY, HF_TOKEN + + return jsonify({ + 'status': 'ok', + 'service': 'JanSahayak', + 'api_keys_configured': { + 'groq': bool(GROQ_API_KEY), + 'tavily': bool(TAVILY_API_KEY), + 'hf_token': bool(HF_TOKEN) + } + }) + + +@app.route('/analyze', methods=['POST']) +def analyze(): + """Process user input and run workflow""" + try: + # First check if API keys are configured + from config import GROQ_API_KEY, TAVILY_API_KEY + + if not GROQ_API_KEY or GROQ_API_KEY == "": + return jsonify({ + 'success': False, + 'error': 'GROQ_API_KEY is not configured. Please set environment variables on Render.' + }), 500 + + if not TAVILY_API_KEY or TAVILY_API_KEY == "": + return jsonify({ + 'success': False, + 'error': 'TAVILY_API_KEY is not configured. Please set environment variables on Render.' + }), 500 + + # Initialize vectorstores lazily on first request + initialize_vectorstores() + + # Get user input + user_input = request.json.get('user_input', '') + structured_data = request.json.get('structured_data', None) + + if not user_input.strip(): + return jsonify({ + 'success': False, + 'error': 'Please provide your details' + }), 400 + + # Generate session ID + session_id = str(uuid.uuid4()) + + # Store in session (including structured data if available) + sessions[session_id] = { + 'status': 'processing', + 'input': user_input, + 'structured_data': structured_data, + 'started_at': datetime.now().isoformat() + } + + # Extract user interests from structured data + user_interests = structured_data.get('interests', ['schemes', 'exams']) if structured_data else ['schemes', 'exams'] + + # Prepare structured profile if available + structured_profile = None + if structured_data: + structured_profile = { + 'name': structured_data.get('name', 'Not Provided'), + 'age': structured_data.get('age', 'Not Provided'), + 'gender': structured_data.get('gender', 'Not Provided'), + 'state': structured_data.get('state', 'Not Provided'), + 'education': structured_data.get('education', 'Not Provided'), + 'employment_status': structured_data.get('employment', 'Not Provided'), + 'income': structured_data.get('income', 'Not Provided'), + 'caste': structured_data.get('category', 'Not Provided'), + 'specialization': structured_data.get('specialization', 'Not Provided'), + 'career_interest': structured_data.get('career_interest', 'Not Provided'), + 'interests': structured_data.get('interests', []) + } + + # Run workflow with interests, structured profile, and pre-loaded vectorstores + result = run_workflow( + user_input, + user_interests, + structured_profile, + scheme_vectorstore=SCHEME_VECTORSTORE, + exam_vectorstore=EXAM_VECTORSTORE + ) + + # Ensure user_profile key exists in result + if 'user_profile' not in result and 'profile' in result: + result['user_profile'] = result['profile'] + + # Update session + sessions[session_id]['status'] = 'completed' + sessions[session_id]['result'] = result + sessions[session_id]['completed_at'] = datetime.now().isoformat() + + # Save to file + timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") + filename = f"outputs/results_{timestamp}.json" + os.makedirs('outputs', exist_ok=True) + + with open(filename, 'w', encoding='utf-8') as f: + json.dump(result, f, indent=2, ensure_ascii=False) + + return jsonify({ + 'success': True, + 'session_id': session_id, + 'result': result, + 'filename': filename + }) + + except ImportError as e: + print(f"Import Error in /analyze: {str(e)}") + return jsonify({ + 'success': False, + 'error': f'Configuration error: {str(e)}. Please ensure all dependencies are installed.' + }), 500 + except TimeoutError as e: + print(f"Timeout Error in /analyze: {str(e)}") + return jsonify({ + 'success': False, + 'error': 'Request timed out. The analysis is taking longer than expected. Please try again.' + }), 504 + except Exception as e: + print(f"Error in /analyze: {str(e)}") + import traceback + traceback.print_exc() + return jsonify({ + 'success': False, + 'error': f'An error occurred during analysis: {str(e)}' + }), 500 + + +@app.route('/result/Multi-Agent Government Intelligence System
++ JanSahayak (जन सहायक - "People's Assistant") is an advanced AI-powered system designed to help + Indian citizens discover and access government benefits, schemes, and competitive exam opportunities. + Using state-of-the-art multi-agent architecture, we analyze your profile and provide personalized + recommendations. +
+AI extracts structured information from your input using natural language processing
+RAG (Retrieval Augmented Generation) + live web search finds relevant schemes and exams
+7 specialized AI agents work together to provide tailored recommendations
+Calculate missed benefits and get actionable insights for future applications
+Extracts and structures user information for accurate eligibility matching
+Recommends government schemes based on eligibility criteria
+Suggests competitive exams aligned with your education and interests
+Performs live searches on government websites for latest information
+Retrieves relevant information from vector database
+Processes PDFs and images for text extraction
+Estimates missed benefits and provides financial insights
+Fast LLM inference
+Agent framework
+Workflow orchestration
+Vector search
+Live web search
+Backend system
+View your previous analyses and download reports
+No profile data available
+ {% endif %} +Your analysis history will appear here once you run your first analysis
+ + Start Your First Analysis + ++ Get personalized recommendations for government schemes, competitive exams, + and calculate benefits you might have missed - all powered by advanced AI agents. +
+AI Agents
+Schemes
+Exams
+Fill out the form step by step to get personalized recommendations
+AI extracts and structures your information for accurate matching
+RAG + Live web search finds the most relevant schemes and exams
+Get tailored recommendations with eligibility details and application steps
+Discover what you missed and what you can claim now
+No profile information extracted. Please provide more details in your input.
+ {% endif %} + {% else %} +Profile information not available
+ {% endif %} +No scheme recommendations available. Please add scheme PDFs to the database.
+No exam recommendations available. Please add exam PDFs to the database.
+Benefit calculation requires valid scheme data.
+