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| import streamlit as st | |
| import asyncio | |
| import pandas as pd | |
| from typing import List | |
| import os | |
| import json | |
| from datetime import datetime | |
| from dotenv import load_dotenv | |
| from multi_agent_system import MultiAgentSystem, SearchResult | |
| from paper_writer import ResearchPaperWriter | |
| from docx import Document | |
| from docx.shared import Pt, Inches, RGBColor | |
| from docx.enum.text import WD_ALIGN_PARAGRAPH | |
| import io | |
| # Load environment variables | |
| load_dotenv() | |
| # Configure page settings | |
| st.set_page_config( | |
| page_title="Research Assistant", | |
| page_icon="🔍", | |
| layout="wide", | |
| initial_sidebar_state="expanded" | |
| ) | |
| # Custom CSS for improved UI | |
| st.markdown(""" | |
| <style> | |
| /* Main app styling */ | |
| .main { | |
| background-color: #0E1117; | |
| } | |
| /* Headers */ | |
| h1 { | |
| color: #FFFFFF !important; | |
| font-family: 'Segoe UI', sans-serif; | |
| font-weight: 700; | |
| letter-spacing: -0.5px; | |
| padding-bottom: 10px; | |
| } | |
| h2 { | |
| color: #E0E0E0 !important; | |
| font-family: 'Segoe UI', sans-serif; | |
| font-weight: 600; | |
| padding-top: 20px; | |
| padding-bottom: 10px; | |
| } | |
| h3 { | |
| color: #B0B0B0 !important; | |
| font-family: 'Segoe UI', sans-serif; | |
| font-weight: 500; | |
| } | |
| /* Paragraphs and text */ | |
| p, li { | |
| font-size: 15px; | |
| line-height: 1.7; | |
| color: #D0D0D0; | |
| } | |
| /* Buttons */ | |
| .stButton>button { | |
| border-radius: 8px; | |
| font-weight: 600; | |
| font-size: 16px; | |
| padding: 12px 24px; | |
| transition: all 0.3s ease; | |
| border: none; | |
| } | |
| .stButton>button:hover { | |
| transform: translateY(-2px); | |
| box-shadow: 0 4px 12px rgba(103, 126, 234, 0.4); | |
| } | |
| /* Input fields */ | |
| .stTextInput>div>div>input { | |
| border-radius: 8px; | |
| border: 2px solid #667eea; | |
| padding: 12px; | |
| font-size: 15px; | |
| background-color: #1E1E1E; | |
| color: #FFFFFF; | |
| } | |
| .stTextInput>div>div>input:focus { | |
| border-color: #764ba2; | |
| box-shadow: 0 0 0 2px rgba(118, 75, 162, 0.2); | |
| } | |
| /* Metrics */ | |
| [data-testid="stMetricValue"] { | |
| font-size: 28px; | |
| font-weight: 700; | |
| color: #667eea; | |
| } | |
| [data-testid="stMetricLabel"] { | |
| font-size: 14px; | |
| font-weight: 500; | |
| color: #B0B0B0; | |
| } | |
| /* Tabs */ | |
| .stTabs [data-baseweb="tab-list"] { | |
| gap: 8px; | |
| background-color: #1A1A1A; | |
| padding: 10px; | |
| border-radius: 10px; | |
| } | |
| .stTabs [data-baseweb="tab"] { | |
| border-radius: 8px; | |
| padding: 12px 20px; | |
| font-weight: 600; | |
| font-size: 15px; | |
| background-color: #2A2A2A; | |
| color: #B0B0B0; | |
| } | |
| .stTabs [aria-selected="true"] { | |
| background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); | |
| color: white; | |
| } | |
| /* Expanders */ | |
| .streamlit-expanderHeader { | |
| background-color: #1E1E1E; | |
| border-radius: 8px; | |
| font-weight: 600; | |
| color: #E0E0E0; | |
| border-left: 4px solid #667eea; | |
| } | |
| .streamlit-expanderHeader:hover { | |
| background-color: #2A2A2A; | |
| } | |
| /* Dataframes */ | |
| .dataframe { | |
| border-radius: 8px; | |
| overflow: hidden; | |
| } | |
| /* Progress bar */ | |
| .stProgress > div > div > div > div { | |
| background: linear-gradient(90deg, #667eea 0%, #764ba2 100%); | |
| } | |
| /* Success/Info/Warning/Error boxes */ | |
| .stSuccess, .stInfo, .stWarning, .stError { | |
| border-radius: 8px; | |
| padding: 15px; | |
| border-left: 4px solid; | |
| } | |
| /* Sidebar */ | |
| section[data-testid="stSidebar"] { | |
| background-color: #0A0A0A; | |
| } | |
| /* Download buttons */ | |
| .stDownloadButton>button { | |
| background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); | |
| color: white; | |
| border: none; | |
| border-radius: 8px; | |
| font-weight: 600; | |
| } | |
| /* Dividers */ | |
| hr { | |
| border-color: #2A2A2A; | |
| margin: 30px 0; | |
| } | |
| </style> | |
| """, unsafe_allow_html=True) | |
| def init_session_state(): | |
| """Initialize session state variables""" | |
| if 'search_history' not in st.session_state: | |
| st.session_state.search_history = [] | |
| if 'analysis_progress' not in st.session_state: | |
| st.session_state.analysis_progress = 0 | |
| def get_score_badge(score, label): | |
| """Generate color-coded badge for scores""" | |
| try: | |
| score_val = float(score) | |
| if score_val >= 8: | |
| color = "#00C853" # Green | |
| emoji = "🟢" | |
| elif score_val >= 6: | |
| color = "#FFA726" # Orange | |
| emoji = "🟡" | |
| else: | |
| color = "#EF5350" # Red | |
| emoji = "🔴" | |
| return f""" | |
| <div style="display: inline-block; background-color: {color}; color: white; padding: 5px 12px; | |
| border-radius: 15px; margin: 3px; font-weight: bold; font-size: 14px;"> | |
| {emoji} {label}: {score_val:.1f}/10 | |
| </div> | |
| """ | |
| except: | |
| return f"<span>{label}: {score}</span>" | |
| def parse_literature_review(review_text): | |
| """Parse literature review into collapsible sections""" | |
| sections = {} | |
| current_section = "Introduction" | |
| current_content = [] | |
| lines = review_text.split('\n') | |
| for line in lines: | |
| # Detect section headers | |
| if line.startswith('##'): | |
| # Save previous section | |
| if current_content: | |
| sections[current_section] = '\n'.join(current_content) | |
| # Start new section | |
| current_section = line.replace('#', '').strip() | |
| current_content = [] | |
| else: | |
| current_content.append(line) | |
| # Save last section | |
| if current_content: | |
| sections[current_section] = '\n'.join(current_content) | |
| return sections if sections else {"Full Review": review_text} | |
| def convert_markdown_to_docx(markdown_text: str, title: str = "Research Paper") -> bytes: | |
| """Convert markdown text to Word document (.docx) format""" | |
| try: | |
| doc = Document() | |
| # Set document margins | |
| sections = doc.sections | |
| for section in sections: | |
| section.top_margin = Inches(1) | |
| section.bottom_margin = Inches(1) | |
| section.left_margin = Inches(1) | |
| section.right_margin = Inches(1) | |
| # Split markdown into lines | |
| lines = markdown_text.split('\n') | |
| for line in lines: | |
| line = line.strip() | |
| if not line: | |
| continue | |
| # Handle headers | |
| if line.startswith('# '): | |
| # Title (H1) | |
| p = doc.add_heading(line[2:], level=1) | |
| p.alignment = WD_ALIGN_PARAGRAPH.CENTER | |
| elif line.startswith('## '): | |
| # Section (H2) | |
| doc.add_heading(line[3:], level=2) | |
| elif line.startswith('### '): | |
| # Subsection (H3) | |
| doc.add_heading(line[4:], level=3) | |
| elif line.startswith('#### '): | |
| # Sub-subsection (H4) | |
| doc.add_heading(line[5:], level=4) | |
| elif line.startswith('- ') or line.startswith('* '): | |
| # Bullet point | |
| doc.add_paragraph(line[2:], style='List Bullet') | |
| elif line.startswith('> '): | |
| # Block quote | |
| p = doc.add_paragraph(line[2:]) | |
| p.style = 'Intense Quote' | |
| else: | |
| # Regular paragraph | |
| doc.add_paragraph(line) | |
| # Save to bytes | |
| doc_bytes = io.BytesIO() | |
| doc.save(doc_bytes) | |
| doc_bytes.seek(0) | |
| return doc_bytes.getvalue() | |
| except Exception as e: | |
| st.error(f"Error converting to Word: {str(e)}") | |
| return None | |
| async def run_async_operations(system, search_query, max_results=3): | |
| """Handle async complete analysis operation""" | |
| # Always run full pipeline: Paper Collection → Literature Review → Gap Analysis → Hypothesis Generation | |
| complete_results = await system.run_complete_analysis(search_query) | |
| return {"type": "complete", "data": complete_results} | |
| def main(): | |
| """Main application function""" | |
| st.markdown(""" | |
| <div style="text-align: center; padding: 30px 20px; margin-bottom: 20px; | |
| background: linear-gradient(135deg, #0f2027 0%, #203a43 50%, #2c5364 100%); | |
| border-radius: 12px; border: 1px solid rgba(255,255,255,0.1);"> | |
| <h1 style="font-size: 42px; margin-bottom: 8px; color: #ffffff; font-weight: 700; letter-spacing: 0.5px;"> | |
| ResearchForge | |
| </h1> | |
| <p style="font-size: 16px; color: #a0aec0; font-weight: 500; margin-bottom: 15px;"> | |
| Autonomous AI Research Assistant | |
| </p> | |
| <p style="font-size: 14px; color: #cbd5e0; font-weight: 400; line-height: 1.8;"> | |
| Automated Literature Review • Gap Analysis • Hypothesis Generation<br/> | |
| Experiment Design • Dataset Recommendations • AI Research Paper Writer | |
| </p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| init_session_state() | |
| if 'system' not in st.session_state: | |
| with st.spinner("🔄 Initializing Research Assistant... Please wait..."): | |
| st.session_state.system = MultiAgentSystem() | |
| st.success("✅ System initialized! Models will load when needed.") | |
| system = st.session_state.system | |
| # Sidebar configuration | |
| with st.sidebar: | |
| st.markdown(""" | |
| <div style="background: linear-gradient(135deg, #2c5364 0%, #203a43 100%); | |
| padding: 18px; border-radius: 8px; margin-bottom: 20px; border: 1px solid rgba(255,255,255,0.1);"> | |
| <h2 style="color: white; margin: 0; text-align: center; font-size: 20px; font-weight: 600;">Configuration</h2> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| st.markdown("### Analysis Pipeline") | |
| st.markdown(""" | |
| <div style="background-color: #1e293b; padding: 15px; border-radius: 8px; border-left: 3px solid #3b82f6;"> | |
| <p style="color: #e2e8f0; font-size: 14px; margin-bottom: 10px; font-weight: 600;">What happens when you search:</p> | |
| <p style="color: #cbd5e0; margin: 6px 0; font-size: 13px;">1. 📄 Collect 10-15 research papers</p> | |
| <p style="color: #cbd5e0; margin: 6px 0; font-size: 13px;">2. 📚 Literature Review (GPT-3.5)</p> | |
| <p style="color: #cbd5e0; margin: 6px 0; font-size: 13px;">3. 🔍 Gap Analysis (GPT-4)</p> | |
| <p style="color: #cbd5e0; margin: 6px 0; font-size: 13px;">4. 💡 Hypothesis Generation (GPT-4)</p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| st.markdown("") | |
| # API Status | |
| st.markdown("### API Status") | |
| if os.getenv('OPENAI_API_KEY') and os.getenv('GOOGLE_SEARCH_ENGINE_ID'): | |
| st.markdown(""" | |
| <div style="background-color: #064e3b; padding: 12px; border-radius: 8px; border-left: 3px solid #10b981;"> | |
| <p style="color: #d1fae5; margin: 0; font-weight: 600; font-size: 14px;">✅ All APIs configured</p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| else: | |
| st.markdown(""" | |
| <div style="background-color: #7f1d1d; padding: 12px; border-radius: 8px; border-left: 3px solid #ef4444;"> | |
| <p style="color: #fecaca; margin: 0; font-weight: 600; font-size: 14px;">❌ APIs not configured. Please check .env file</p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| st.markdown("<br>", unsafe_allow_html=True) | |
| st.markdown("---") | |
| st.markdown(""" | |
| <div style="background-color: #1e293b; padding: 15px; border-radius: 8px;"> | |
| <p style="color: #60a5fa; font-weight: 600; margin-bottom: 10px; font-size: 14px;">Powered by:</p> | |
| <p style="color: #e2e8f0; margin: 5px 0; padding-left: 10px; font-size: 13px;">• OpenAI GPT-4 & GPT-3.5</p> | |
| <p style="color: #e2e8f0; margin: 5px 0; padding-left: 10px; font-size: 13px;">• Sentence-BERT</p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # Add info about model loading | |
| st.markdown("<br>", unsafe_allow_html=True) | |
| st.markdown(""" | |
| <div style="background-color: #134e4a; padding: 12px; border-radius: 8px; border-left: 3px solid #14b8a6;"> | |
| <p style="color: #5eead4; margin: 0; font-size: 13px;">💡 <b>Note:</b> Heavy AI models load on first use. Subsequent uses are instant!</p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| # Main search interface | |
| st.markdown("") | |
| st.markdown("") | |
| # Show current analysis status | |
| if st.session_state.get('complete_data'): | |
| current_topic = st.session_state.get('search_query', 'Unknown') | |
| st.markdown(f""" | |
| <div style="background-color: rgba(16, 185, 129, 0.1); padding: 12px 20px; border-radius: 6px; | |
| border-left: 3px solid #10b981; margin-bottom: 20px;"> | |
| <p style="color: #6ee7b7; margin: 0; font-size: 14px; font-weight: 500;"> | |
| ✓ Analysis completed for: <span style="color: #d1fae5;">{current_topic}</span> | |
| </p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| col1, col2 = st.columns([3, 1]) | |
| with col2: | |
| if st.button("🔄 New Analysis", type="secondary", use_container_width=True): | |
| # Clear all session state | |
| for key in ['complete_data', 'search_query', 'selected_hypothesis', 'selected_gap', | |
| 'experiment_results', 'selected_experiment', 'generated_paper']: | |
| if key in st.session_state: | |
| del st.session_state[key] | |
| st.rerun() | |
| # Clean, minimal search input | |
| st.markdown(""" | |
| <p style="color: #cbd5e0; font-size: 15px; font-weight: 500; margin-bottom: 10px;"> | |
| 🔍 Enter Your Research Topic | |
| </p> | |
| """, unsafe_allow_html=True) | |
| search_query = st.text_input( | |
| "Research Topic", | |
| placeholder="e.g., machine learning for drug discovery, quantum computing applications...", | |
| label_visibility="collapsed", | |
| key="search_input" | |
| ) | |
| search_button = st.button("🚀 Start Analysis", type="primary", use_container_width=True) | |
| st.markdown("<br>", unsafe_allow_html=True) | |
| # Handle search | |
| if search_button and search_query: | |
| # Create progress bar | |
| progress_bar = st.progress(0) | |
| status_text = st.empty() | |
| try: | |
| # Phase 1: Paper Collection | |
| status_text.markdown(""" | |
| <div style="padding: 10px 15px; border-left: 3px solid #3b82f6; background-color: rgba(59, 130, 246, 0.1);"> | |
| <p style="color: #93c5fd; margin: 0; font-size: 14px;">📄 Phase 1/4: Collecting research papers...</p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| progress_bar.progress(10) | |
| system = MultiAgentSystem() | |
| # Phase 2: Literature Review | |
| status_text.markdown(""" | |
| <div style="padding: 10px 15px; border-left: 3px solid #10b981; background-color: rgba(16, 185, 129, 0.1);"> | |
| <p style="color: #6ee7b7; margin: 0; font-size: 14px;">📚 Phase 2/4: Analyzing literature...</p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| progress_bar.progress(30) | |
| # Run analysis (this includes all phases) | |
| result = asyncio.run(run_async_operations( | |
| system, | |
| search_query, | |
| max_results=5 | |
| )) | |
| # Phase 3: Gap Analysis | |
| status_text.markdown(""" | |
| <div style="padding: 10px 15px; border-left: 3px solid #f59e0b; background-color: rgba(245, 158, 11, 0.1);"> | |
| <p style="color: #fcd34d; margin: 0; font-size: 14px;">🔍 Phase 3/4: Identifying research gaps...</p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| progress_bar.progress(65) | |
| # Phase 4: Hypothesis Generation | |
| status_text.markdown(""" | |
| <div style="padding: 10px 15px; border-left: 3px solid #8b5cf6; background-color: rgba(139, 92, 246, 0.1);"> | |
| <p style="color: #c4b5fd; margin: 0; font-size: 14px;">💡 Phase 4/4: Generating hypotheses...</p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| progress_bar.progress(90) | |
| # Complete | |
| complete_data = result["data"] | |
| st.session_state['complete_data'] = complete_data | |
| st.session_state['search_query'] = search_query | |
| progress_bar.progress(100) | |
| status_text.markdown(""" | |
| <div style="padding: 12px 20px; border-left: 3px solid #10b981; background-color: rgba(16, 185, 129, 0.15);"> | |
| <p style="color: #6ee7b7; margin: 0; font-size: 15px; font-weight: 600;"> | |
| ✓ Analysis Complete | |
| </p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| st.balloons() | |
| except Exception as e: | |
| st.error(f"An error occurred: {str(e)}") | |
| st.error(f"Error details: {type(e).__name__}") | |
| # Display tabs if we have complete data in session state | |
| if st.session_state.get('complete_data'): | |
| complete_data = st.session_state['complete_data'] | |
| search_query = st.session_state.get('search_query', '') | |
| # Create tabs for different sections | |
| tab1, tab2, tab3, tab4, tab5 = st.tabs([ | |
| "📚 Literature Review", | |
| "🔍 Gap Analysis", | |
| "💡 Hypotheses", | |
| "🧪 Experiment Design", | |
| "📝 AI Paper Writer" | |
| ]) | |
| with tab1: | |
| st.header("Literature Review") | |
| # Show papers collected | |
| papers = complete_data.get("papers", []) | |
| if papers: | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| st.metric("📄 Papers Found", len(papers)) | |
| with col2: | |
| arxiv_count = sum(1 for p in papers if hasattr(p, 'arxiv_url') and p.arxiv_url) | |
| st.metric("📚 arXiv Papers", arxiv_count) | |
| with col3: | |
| google_count = len(papers) - arxiv_count | |
| st.metric("🔍 Google Scholar", google_count) | |
| st.markdown("---") | |
| # Show literature review with collapsible sections | |
| if complete_data.get("literature_review"): | |
| review_text = complete_data["literature_review"] | |
| # Download button for literature review | |
| col1, col2 = st.columns([3, 1]) | |
| with col2: | |
| st.download_button( | |
| label="📥 Download as TXT", | |
| data=review_text, | |
| file_name=f"literature_review_{datetime.now().strftime('%Y%m%d')}.txt", | |
| mime="text/plain", | |
| use_container_width=True | |
| ) | |
| # Parse into sections | |
| sections = parse_literature_review(review_text) | |
| if len(sections) > 1: | |
| st.markdown("### 📖 Review Sections (Click to Expand)") | |
| for section_name, section_content in sections.items(): | |
| with st.expander(f"📑 {section_name}", expanded=(section_name == "Introduction" or section_name == "Full Review")): | |
| st.markdown(section_content) | |
| else: | |
| st.markdown(review_text) | |
| else: | |
| st.warning("No literature review available") | |
| # Display collected papers with links | |
| if papers: | |
| st.markdown("---") | |
| st.subheader("📚 Research Papers Analyzed") | |
| st.info(f"💡 {len(papers)} papers were collected and analyzed for this review") | |
| for i, paper in enumerate(papers, 1): | |
| with st.expander(f"📄 Paper {i}: {paper.title}", expanded=False): | |
| if hasattr(paper, 'authors') and paper.authors: | |
| st.markdown(f"**✍️ Authors:** {', '.join(paper.authors[:3])}{'...' if len(paper.authors) > 3 else ''}") | |
| if hasattr(paper, 'published') and paper.published: | |
| st.markdown(f"**📅 Published:** {paper.published}") | |
| if hasattr(paper, 'abstract') and paper.abstract: | |
| st.markdown(f"**📝 Abstract:**") | |
| st.markdown(paper.abstract[:500] + "..." if len(paper.abstract) > 500 else paper.abstract) | |
| elif hasattr(paper, 'snippet') and paper.snippet: | |
| st.markdown(f"**📝 Summary:**") | |
| st.markdown(paper.snippet) | |
| # Links | |
| links = [] | |
| if hasattr(paper, 'arxiv_url') and paper.arxiv_url: | |
| links.append(f"[📄 arXiv]({paper.arxiv_url})") | |
| if hasattr(paper, 'pdf_url') and paper.pdf_url: | |
| links.append(f"[📥 PDF]({paper.pdf_url})") | |
| if hasattr(paper, 'link') and paper.link: | |
| links.append(f"[🔗 Source]({paper.link})") | |
| if links: | |
| st.markdown("**🔗 Links:** " + " | ".join(links)) | |
| else: | |
| st.warning("No papers were collected for this analysis") | |
| with tab2: | |
| st.header("Research Gap Analysis") | |
| gap_data = complete_data.get("gap_analysis", {}) | |
| # Show which model was used | |
| gaps_dict = gap_data.get("gaps", {}) | |
| if gaps_dict.get("source"): | |
| if "Combined" in gaps_dict["source"]: | |
| st.success(f"🤖 Analysis by: {gaps_dict['source']}") | |
| elif "GPT-4" in gaps_dict["source"]: | |
| st.info(f"🤖 Analysis by: {gaps_dict['source']}") | |
| else: | |
| st.success(f"🤖 Analysis by: {gaps_dict['source']}") | |
| # Show summary metrics | |
| col1, col2, col3, col4 = st.columns(4) | |
| with col1: | |
| st.metric("Papers Analyzed", gap_data.get("papers_analyzed", 0)) | |
| with col2: | |
| total_gaps = sum(len(gaps_dict.get(k, [])) for k in ["knowledge_gaps", "methodological_gaps", "dataset_gaps", "temporal_gaps"]) | |
| st.metric("Total Gaps Found", total_gaps) | |
| with col3: | |
| st.metric("Categories", 4) | |
| with col4: | |
| # Download button | |
| gap_text = gap_data.get("formatted_output", "No gap analysis available") | |
| st.download_button( | |
| label="📥 Download", | |
| data=gap_text, | |
| file_name=f"gap_analysis_{datetime.now().strftime('%Y%m%d')}.txt", | |
| mime="text/plain", | |
| use_container_width=True | |
| ) | |
| # Display formatted gap analysis | |
| st.markdown(gap_data.get("formatted_output", "No gap analysis available")) | |
| with tab3: | |
| st.header("Research Hypotheses") | |
| hyp_data = complete_data.get("hypotheses", {}) | |
| if hyp_data.get("hypotheses"): | |
| hypotheses_list = hyp_data.get("hypotheses", []) | |
| # Show summary metrics | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| st.metric("Hypotheses Generated", len(hypotheses_list)) | |
| with col2: | |
| avg_score = sum(h.get("overall_score", 0) for h in hypotheses_list) / max(len(hypotheses_list), 1) | |
| st.metric("Avg Overall Score", f"{avg_score:.2f}") | |
| with col3: | |
| avg_novelty = sum(h.get("novelty_score", 0) for h in hypotheses_list) / max(len(hypotheses_list), 1) | |
| st.metric("Avg Novelty Score", f"{avg_novelty:.2f}") | |
| st.markdown("---") | |
| # Side-by-side comparison table | |
| st.markdown("### 📊 Hypothesis Comparison Table") | |
| comparison_data = [] | |
| for idx, hyp in enumerate(hypotheses_list, 1): | |
| comparison_data.append({ | |
| "#": idx, | |
| "Title": hyp.get('title', 'Untitled')[:50] + '...' if len(hyp.get('title', '')) > 50 else hyp.get('title', 'Untitled'), | |
| "Overall": f"{hyp.get('overall_score', 0):.1f}", | |
| "Novelty": f"{hyp.get('novelty_score', 0):.1f}", | |
| "Impact": f"{hyp.get('impact_score', 0):.1f}", | |
| "Feasibility": f"{hyp.get('feasibility_score', 0):.1f}", | |
| "Gap": hyp.get('gap_addressed', 'N/A')[:40] + '...' if len(hyp.get('gap_addressed', '')) > 40 else hyp.get('gap_addressed', 'N/A') | |
| }) | |
| comparison_df = pd.DataFrame(comparison_data) | |
| st.dataframe(comparison_df, use_container_width=True, hide_index=True) | |
| # Download CSV button | |
| csv = comparison_df.to_csv(index=False) | |
| st.download_button( | |
| label="📥 Download Comparison as CSV", | |
| data=csv, | |
| file_name=f"hypothesis_comparison_{datetime.now().strftime('%Y%m%d')}.csv", | |
| mime="text/csv", | |
| use_container_width=True | |
| ) | |
| st.markdown("---") | |
| # Display hypotheses with selection and color-coded badges | |
| st.markdown("### 💡 Detailed Hypotheses (Click to Expand)") | |
| for idx, hyp in enumerate(hypotheses_list, 1): | |
| with st.expander(f"{'🔴' if hyp.get('priority') == 'HIGH' else '🟡'} Hypothesis {idx}: {hyp.get('title', 'Untitled')}", expanded=(idx==1)): | |
| # Color-coded score badges | |
| st.markdown("**📊 Performance Scores:**", unsafe_allow_html=True) | |
| badges_html = "" | |
| badges_html += get_score_badge(hyp.get('overall_score', 0), 'Overall') | |
| badges_html += get_score_badge(hyp.get('novelty_score', 0), 'Novelty') | |
| badges_html += get_score_badge(hyp.get('impact_score', 0), 'Impact') | |
| badges_html += get_score_badge(hyp.get('feasibility_score', 0), 'Feasibility') | |
| st.markdown(badges_html, unsafe_allow_html=True) | |
| st.markdown("") | |
| if hyp.get('gap_addressed'): | |
| st.markdown(f"**🎯 Gap Addressed:** {hyp['gap_addressed']}") | |
| if hyp.get('problem_statement'): | |
| st.markdown(f"**❓ Problem Statement:** {hyp['problem_statement']}") | |
| if hyp.get('proposed_solution'): | |
| st.markdown(f"**💡 Proposed Solution:** {hyp['proposed_solution']}") | |
| if hyp.get('expected_impact'): | |
| st.markdown(f"**🚀 Expected Impact:** {hyp['expected_impact']}") | |
| else: | |
| st.warning("No hypotheses generated") | |
| with tab4: | |
| st.header("🧪 Experiment Design") | |
| # Check if we have hypotheses to work with | |
| if complete_data.get('hypotheses'): | |
| hypotheses = complete_data['hypotheses'].get('hypotheses', []) | |
| if hypotheses: | |
| # Hypothesis selection | |
| st.subheader("Select a Hypothesis") | |
| hypothesis_options = [f"Hypothesis {h.get('hypothesis_id', i+1)}: {h.get('title', 'Untitled')}" | |
| for i, h in enumerate(hypotheses)] | |
| selected_index = st.selectbox( | |
| "Choose a hypothesis to generate experiments for:", | |
| range(len(hypothesis_options)), | |
| format_func=lambda x: hypothesis_options[x], | |
| key="hypothesis_selector" | |
| ) | |
| selected_hypothesis = hypotheses[selected_index] | |
| # Store selected hypothesis and its gap in session state | |
| st.session_state['selected_hypothesis'] = selected_hypothesis | |
| st.session_state['selected_gap'] = selected_hypothesis.get('gap_addressed', 'Not specified') | |
| # Show selected hypothesis details | |
| with st.expander("📋 Selected Hypothesis Details", expanded=False): | |
| st.write(f"**Title:** {selected_hypothesis.get('title', 'N/A')}") | |
| st.write(f"**Gap Addressed:** {selected_hypothesis.get('gap_addressed', 'N/A')}") | |
| st.write(f"**Problem Statement:** {selected_hypothesis.get('problem_statement', 'N/A')}") | |
| st.write(f"**Proposed Solution:** {selected_hypothesis.get('proposed_solution', 'N/A')}") | |
| col1, col2, col3 = st.columns(3) | |
| with col1: | |
| st.metric("Novelty Score", f"{selected_hypothesis.get('novelty_score', 'N/A')}/10") | |
| with col2: | |
| st.metric("Impact Score", f"{selected_hypothesis.get('impact_score', 'N/A')}/10") | |
| with col3: | |
| st.metric("Feasibility Score", f"{selected_hypothesis.get('feasibility_score', 'N/A')}/10") | |
| # Generate Experiments Button | |
| if st.button("🚀 Generate Experiments & Datasets", key="generate_experiments"): | |
| with st.spinner("🧪 Generating experiments, datasets, and metrics..."): | |
| try: | |
| experiment_results = asyncio.run( | |
| system.generate_experiments_for_hypothesis( | |
| selected_hypothesis, | |
| search_query | |
| ) | |
| ) | |
| st.session_state['experiment_results'] = experiment_results | |
| st.session_state['selected_experiment'] = None # Reset selection | |
| st.success("✅ Experiments generated successfully!") | |
| except Exception as e: | |
| st.error(f"❌ Error generating experiments: {str(e)}") | |
| # Display experiment results | |
| if st.session_state.get('experiment_results'): | |
| st.markdown("---") | |
| display_experiment_results(st.session_state['experiment_results']) | |
| else: | |
| st.warning("⚠️ No hypotheses available. Please run the complete analysis first.") | |
| else: | |
| st.info("👈 Run a complete analysis first to generate hypotheses.") | |
| with tab5: | |
| st.header("📝 AI Research Paper Writer") | |
| st.markdown("*Generate a complete, publication-ready research paper in Word format*") | |
| # Check if all required data is selected | |
| selected_hypothesis = st.session_state.get('selected_hypothesis') | |
| selected_experiment = st.session_state.get('selected_experiment') | |
| selected_gap = st.session_state.get('selected_gap', 'Not specified') | |
| if selected_hypothesis and selected_experiment: | |
| # Show ready status | |
| st.success("✅ All required components selected! Ready to generate your paper.") | |
| st.markdown("---") | |
| # Show selected data summary in card-like layout | |
| st.markdown("### 📋 Your Research Paper Components:") | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| with st.container(): | |
| st.markdown("**📊 Research Topic**") | |
| st.info(complete_data.get('topic', 'Unknown')) | |
| st.markdown("**💡 Hypothesis**") | |
| st.info(selected_hypothesis.get('title', 'Unknown')) | |
| with col2: | |
| with st.container(): | |
| st.markdown("**🧪 Experiment**") | |
| st.info(f"{selected_experiment.get('title', 'Unknown')}") | |
| st.markdown("**🔍 Gap Addressed**") | |
| st.info(selected_gap) | |
| st.markdown("---") | |
| # Show what will be included | |
| with st.expander("📄 What's included in your paper?", expanded=False): | |
| col_a, col_b = st.columns(2) | |
| with col_a: | |
| st.markdown(""" | |
| **Title & Abstract** | |
| - Title: 10-12 words based on your hypothesis | |
| - Abstract: ~200 words summarizing the paper | |
| **Introduction** (~550 words, 4 paragraphs) | |
| - Background on the research topic | |
| - Problem statement from hypothesis | |
| - Research objectives and contributions | |
| - Paper structure overview | |
| **Literature Review** (~700 words, 4-5 paragraphs) | |
| - Analysis of collected research papers | |
| - Current state of the art | |
| - Research gaps identified | |
| - Positioning of your work | |
| **Methodology** (~800 words, 5-6 paragraphs) | |
| - Complete experimental design | |
| - Step-by-step procedures from selected experiment | |
| - Datasets, metrics, and evaluation approach | |
| - Implementation details | |
| """) | |
| with col_b: | |
| st.markdown(""" | |
| **Expected Results** (~550 words, 4 paragraphs) | |
| - Anticipated experimental outcomes | |
| - Tables and figures templates | |
| - Performance metrics | |
| - Analysis framework | |
| **Discussion** (~600 words, 4-5 paragraphs) | |
| - Interpretation of expected results | |
| - Comparison with baselines | |
| - Implications of findings | |
| - Limitations and strengths | |
| **Conclusion** (~300 words, 3 paragraphs) | |
| - Summary of contributions | |
| - Key findings | |
| - Future work directions | |
| **References** | |
| - Formatted citations (APA style) | |
| - All collected research papers | |
| **Total: ~3,500 words (4-5 pages)** | |
| """) | |
| st.markdown("---") | |
| # Initialize paper writer | |
| writer = ResearchPaperWriter() | |
| # Context for generation | |
| paper_context = { | |
| 'topic': complete_data.get('topic', ''), | |
| 'hypothesis': selected_hypothesis, | |
| 'gap': selected_gap, | |
| 'experiment': selected_experiment, | |
| 'papers': complete_data.get('papers', []), | |
| 'literature_review': complete_data.get('literature_review', ''), | |
| 'gaps': complete_data.get('gap_analysis', {}).get('gaps', {}) | |
| } | |
| # Generate Paper Button | |
| st.markdown("### 🚀 Generate Complete Research Paper") | |
| st.info("💡 Click the button below to generate a full research paper template. This will take 2-3 minutes.") | |
| if st.button("📝 Generate Research Paper Template", type="primary", use_container_width=True, key="generate_paper"): | |
| with st.spinner("🤖 AI is writing your complete research paper... Please wait 2-3 minutes"): | |
| try: | |
| # Generate complete paper | |
| complete_paper_md = writer.generate_complete_paper(paper_context) | |
| # Store in session state | |
| st.session_state['generated_paper'] = complete_paper_md | |
| st.success("✅ Research paper generated successfully!") | |
| st.rerun() | |
| except Exception as e: | |
| st.error(f"❌ Error generating paper: {str(e)}") | |
| # Display and download generated paper | |
| if st.session_state.get('generated_paper'): | |
| st.markdown("---") | |
| st.markdown("### 📄 Your Generated Research Paper") | |
| # Paper stats | |
| paper_text = st.session_state['generated_paper'] | |
| word_count = len(paper_text.split()) | |
| pages = word_count // 250 | |
| col1, col2, col3, col4 = st.columns(4) | |
| with col1: | |
| st.metric("Total Words", f"{word_count:,}") | |
| with col2: | |
| st.metric("Est. Pages", f"~{pages}") | |
| with col3: | |
| st.metric("Sections", "8") | |
| with col4: | |
| st.metric("Status", "✅ Ready") | |
| st.markdown("") | |
| # Preview | |
| with st.expander("👁️ Preview Paper Content", expanded=False): | |
| st.markdown(paper_text[:3000] + "..." if len(paper_text) > 3000 else paper_text) | |
| st.markdown("---") | |
| st.markdown("### 📥 Download Your Research Paper") | |
| st.info("💡 Your paper will be downloaded as a Microsoft Word document (.docx) ready for editing and submission!") | |
| # Convert to Word document | |
| try: | |
| paper_title = selected_hypothesis.get('title', 'Research Paper') | |
| doc_bytes = convert_markdown_to_docx(paper_text, paper_title) | |
| if doc_bytes: | |
| col1, col2, col3 = st.columns([1, 2, 1]) | |
| with col2: | |
| st.download_button( | |
| label="📄 Download as Word Document (.docx)", | |
| data=doc_bytes, | |
| file_name=f"research_paper_{datetime.now().strftime('%Y%m%d_%H%M%S')}.docx", | |
| mime="application/vnd.openxmlformats-officedocument.wordprocessingml.document", | |
| use_container_width=True, | |
| type="primary" | |
| ) | |
| else: | |
| st.error("Error converting paper to Word format. Please try again.") | |
| except Exception as e: | |
| st.error(f"Error creating Word document: {str(e)}") | |
| st.info("💡 Tip: Make sure python-docx is installed: pip install python-docx") | |
| st.markdown("") | |
| with st.expander("ℹ️ Next Steps - How to Use Your Paper"): | |
| st.markdown(""" | |
| ### 📝 Your Research Paper Template is Ready! | |
| **What's Included:** | |
| - ✅ Title and Abstract | |
| - ✅ Introduction with background | |
| - ✅ Literature Review from collected papers | |
| - ✅ Complete Methodology from your selected experiment | |
| - ✅ Expected Results section (fill with actual results) | |
| - ✅ Discussion framework | |
| - ✅ Conclusion | |
| - ✅ Formatted References | |
| **How to Complete Your Paper:** | |
| 1. **Download** the Markdown or Text version | |
| 2. **Open** in Word, Google Docs, or any text editor | |
| 3. **Run** your experiment and collect results | |
| 4. **Add** actual results, figures, and tables to Section 4 | |
| 5. **Update** discussion based on real findings | |
| 6. **Proofread** and format for your target journal | |
| 7. **Submit** your completed paper! | |
| **File Formats:** | |
| - **Markdown (.md)**: Best for converting to PDF/LaTeX via Pandoc or Overleaf | |
| - **Text (.txt)**: Universal format, open in any editor | |
| - **JSON**: Contains paper + metadata, machine-readable | |
| **💡 Pro Tip:** The paper is written in academic style using GPT-4 with all your collected research context! | |
| """) | |
| # Clear button | |
| st.markdown("---") | |
| if st.button("🗑️ Clear and Generate New Paper", type="secondary"): | |
| st.session_state['generated_paper'] = None | |
| st.success("✅ Cleared! Click 'Generate' button above to create a new paper.") | |
| st.rerun() | |
| elif selected_hypothesis and not selected_experiment: | |
| st.warning(""" | |
| ### ⚠️ Please Select an Experiment | |
| You have selected a hypothesis, but no experiment yet. | |
| **Steps:** | |
| 1. Go to **Tab 4** (Experiment Design) | |
| 2. Click **"Generate Experiments & Datasets"** if not done | |
| 3. **Select ONE experiment** by clicking "✅ Select This Experiment" | |
| 4. Return to this tab to generate your paper | |
| """) | |
| else: | |
| st.info(""" | |
| ### 👈 Complete Previous Steps First | |
| To generate a research paper template, you need to: | |
| 1. **Tab 1-2**: Run complete analysis to collect papers and identify gaps | |
| 2. **Tab 3**: Review and select a hypothesis | |
| 3. **Tab 4**: Generate experiments and **select ONE experiment** | |
| 4. **Tab 5** (Here): Generate your complete research paper! | |
| The AI will create a professional academic paper including: | |
| - Title, Abstract, Introduction | |
| - Literature Review with citations | |
| - Methodology from your experiment | |
| - Expected Results section (for you to fill) | |
| - Discussion and Conclusion | |
| - Properly formatted References | |
| """) | |
| # Show search history | |
| if st.session_state.search_history: | |
| st.header("Search History") | |
| history_df = pd.DataFrame(st.session_state.search_history) | |
| st.dataframe( | |
| history_df, | |
| hide_index=True, | |
| column_config={ | |
| "timestamp": st.column_config.DatetimeColumn( | |
| "Time", | |
| format="D MMM, YYYY, HH:mm" | |
| ) | |
| } | |
| ) | |
| def display_experiment_results(results: dict): | |
| """Display the experiment generation results in a structured format""" | |
| st.header("🧪 Detailed Experiment Plans") | |
| # Experiments Section | |
| if results.get('experiments'): | |
| st.subheader("🔬 Select an Experiment for Your Research Paper") | |
| st.info("👇 Each experiment contains EVERYTHING you need: introduction, methodology, datasets, metrics, models, and challenges. Choose ONE to use in your paper.") | |
| # Experiment selection | |
| for i, exp in enumerate(results['experiments']): | |
| with st.expander(f"⚗️ Experiment {exp['id']}: {exp['title']}", expanded=(i==0)): | |
| # Header with difficulty and time | |
| col1, col2, col3 = st.columns([2, 1, 1]) | |
| with col1: | |
| st.markdown(f"### 📋 {exp['title']}") | |
| with col2: | |
| difficulty_color = {"Easy": "🟢", "Medium": "🟡", "Hard": "🔴"} | |
| diff = exp.get('difficulty', 'Medium') | |
| st.metric("Difficulty", f"{difficulty_color.get(diff, '⚪')} {diff}") | |
| with col3: | |
| st.metric("Estimated Time", exp.get('estimated_time', '2-3 months')) | |
| st.markdown("---") | |
| # 1. Introduction | |
| if exp.get('introduction'): | |
| st.markdown("**📖 1. Introduction & Background**") | |
| st.markdown(exp['introduction']) | |
| st.markdown("") | |
| # 2. Description | |
| st.markdown("**🎯 2. Experiment Overview**") | |
| st.markdown(exp['description']) | |
| st.markdown("") | |
| # 3. Detailed Methodology Steps | |
| if exp.get('steps'): | |
| st.markdown("**🔬 3. Detailed Methodology (Step-by-Step)**") | |
| for j, step in enumerate(exp['steps'], 1): | |
| st.markdown(f"**Step {j}:** {step}") | |
| st.markdown("") | |
| # 4. Datasets (from results dict, not just experiment) | |
| st.markdown("**📊 4. Recommended Datasets**") | |
| if results.get('datasets'): | |
| datasets = results['datasets'] | |
| # Check if it's a list (new format) or dict (old format) | |
| if isinstance(datasets, list): | |
| # New format: simple list | |
| if datasets: | |
| for idx, dataset in enumerate(datasets[:2], 1): # Show top 2 | |
| st.markdown(f"**{idx}. {dataset['name']}**") | |
| st.markdown(f" - 📦 Size: {dataset.get('size', 'N/A')}") | |
| # Show downloads if available | |
| if dataset.get('downloads'): | |
| downloads = dataset.get('downloads') | |
| if isinstance(downloads, int): | |
| st.markdown(f" - 💾 Downloads: {downloads:,}") | |
| else: | |
| st.markdown(f" - 💾 Downloads: {downloads}") | |
| # Show usability if available | |
| if dataset.get('usability'): | |
| st.markdown(f" - ⭐ Usability: {dataset['usability']:.1f}/10") | |
| # Description | |
| desc = dataset.get('description', '') | |
| if len(desc) > 150: | |
| st.markdown(f" - {desc[:150]}...") | |
| else: | |
| st.markdown(f" - {desc}") | |
| # Link | |
| if dataset.get('url'): | |
| source = dataset.get('source', 'Kaggle') | |
| st.markdown(f" - [📥 **Access Dataset**]({dataset['url']})") | |
| st.markdown("") | |
| else: | |
| st.markdown("• Custom dataset required (see methodology)") | |
| else: | |
| # Old format fallback: dict with kaggle/huggingface keys | |
| kaggle_datasets = datasets.get('kaggle', []) | |
| hf_datasets = datasets.get('huggingface', []) | |
| all_datasets = kaggle_datasets + hf_datasets | |
| if all_datasets: | |
| for idx, dataset in enumerate(all_datasets[:2], 1): | |
| st.markdown(f"**{idx}. {dataset['name']}**") | |
| st.markdown(f" - {dataset.get('description', '')[:150]}") | |
| if dataset.get('url'): | |
| st.markdown(f" - [📥 **Access Dataset**]({dataset['url']})") | |
| st.markdown("") | |
| else: | |
| st.markdown("• Custom dataset required (see methodology)") | |
| else: | |
| st.markdown("• Dataset recommendations will be generated based on your topic") | |
| st.markdown("") | |
| # 5. Evaluation Metrics | |
| if results.get('metrics'): | |
| st.markdown("**📈 5. Evaluation Metrics**") | |
| for metric in results['metrics'][:4]: # Top 4 metrics | |
| st.markdown(f"• **{metric['name']}**: {metric['description']}") | |
| if metric.get('range'): | |
| st.markdown(f" - Range: {metric['range']}") | |
| st.markdown("") | |
| # 6. Model Architectures | |
| if results.get('architectures'): | |
| st.markdown("**🏗️ 6. Suggested Model Architectures**") | |
| for arch in results['architectures'][:3]: # Top 3 architectures | |
| st.markdown(f"• **{arch['name']}**") | |
| st.markdown(f" - {arch['description']}") | |
| if arch.get('parameters'): | |
| st.markdown(f" - Parameters: {arch['parameters']}") | |
| st.markdown("") | |
| # 7. Baseline Models | |
| if results.get('baselines'): | |
| st.markdown("**📊 7. Baseline Models for Comparison**") | |
| for baseline in results['baselines'][:3]: | |
| st.markdown(f"• **{baseline['name']}**: {baseline['description']}") | |
| if baseline.get('expected_performance'): | |
| st.markdown(f" - Expected Performance: {baseline['expected_performance']}") | |
| st.markdown("") | |
| # 8. Expected Outcomes | |
| if results.get('expected_outcomes'): | |
| st.markdown("**🎯 8. Expected Outcomes**") | |
| outcomes = results['expected_outcomes'] | |
| col1, col2, col3, col4 = st.columns(4) | |
| with col1: | |
| st.metric("Target Accuracy", outcomes.get('accuracy_range', '85-92%')) | |
| with col2: | |
| st.metric("Training Time", outcomes.get('training_time', '4-6 hours')) | |
| with col3: | |
| st.metric("Model Size", outcomes.get('model_size', '50-100MB')) | |
| with col4: | |
| st.metric("Inference Speed", outcomes.get('inference_speed', '10-20ms')) | |
| st.markdown("") | |
| # 9. Challenges & Solutions | |
| if results.get('challenges'): | |
| st.markdown("**⚠️ 9. Potential Challenges & Mitigation**") | |
| for challenge in results['challenges'][:4]: | |
| st.markdown(f"• {challenge}") | |
| st.markdown("") | |
| # 10. Required Resources | |
| if exp.get('required_resources'): | |
| st.markdown("**💻 10. Required Resources**") | |
| for resource in exp['required_resources']: | |
| st.markdown(f"• {resource}") | |
| st.markdown("") | |
| # Selection button - prominent | |
| st.markdown("---") | |
| col1, col2, col3 = st.columns([1, 2, 1]) | |
| with col2: | |
| if st.button(f"✓ Select This Experiment for Paper", key=f"select_exp_{exp['id']}", use_container_width=True, type="primary"): | |
| st.session_state['selected_experiment'] = exp | |
| st.success(f"✓ Selected: {exp['title']}") | |
| st.info("→ Go to Tab 5 to generate your research paper!") | |
| # Show currently selected experiment | |
| if st.session_state.get('selected_experiment'): | |
| selected = st.session_state['selected_experiment'] | |
| st.markdown("---") | |
| st.success(f"🎯 **Currently Selected:** Experiment {selected['id']} - {selected['title']}") | |
| st.caption("Go to Tab 5 'AI Paper Writer' to generate your complete research paper template!") | |
| # Download complete experiment plan as JSON | |
| st.markdown("---") | |
| st.subheader("💾 Export Complete Experiment Plan") | |
| json_str = json.dumps(results, indent=2) | |
| st.download_button( | |
| label="📥 Download All Experiments as JSON", | |
| data=json_str, | |
| file_name=f"experiment_plan_{results.get('metadata', {}).get('hypothesis_id', 'unknown')}.json", | |
| mime="application/json", | |
| help="Download all experiment details including datasets, metrics, and models" | |
| ) | |
| def render_footer(): | |
| """Render footer on all pages""" | |
| st.markdown("<br><br>", unsafe_allow_html=True) | |
| st.markdown("---") | |
| st.markdown(""" | |
| <div style="text-align: center; padding: 25px; background-color: #1e293b; | |
| border-radius: 8px; margin-top: 30px; border-top: 2px solid #3b82f6;"> | |
| <p style="color: #60a5fa; font-size: 13px; font-weight: 600; margin-bottom: 12px; text-transform: uppercase; letter-spacing: 1.5px;">Development Team</p> | |
| <p style="color: #cbd5e0; font-size: 15px; margin: 8px 0; line-height: 1.6;"> | |
| Made by <span style="color: #f1f5f9; font-weight: 600;">Radia Riaz</span>, | |
| <span style="color: #f1f5f9; font-weight: 600;">Amna Alvie</span>, | |
| <span style="color: #f1f5f9; font-weight: 600;">Emaan Riaz</span>, | |
| <span style="color: #f1f5f9; font-weight: 600;">Maheen Alvie</span> | |
| </p> | |
| <p style="color: #94a3b8; font-size: 12px; margin-top: 10px;"> | |
| Gen AI Hackathon 2025 | |
| </p> | |
| </div> | |
| """, unsafe_allow_html=True) | |
| if __name__ == "__main__": | |
| main() | |
| render_footer() |