""" app.py — Streamlit frontend for the AI-driven Topic Modeling application. This module provides an interactive web interface that allows users to: 1. Upload a CSV file containing research paper Titles and Abstracts. 2. Configure pipeline parameters (min topics, LLM label generation). 3. Run the TopicAgent pipeline with a single click. 4. View and explore results: topics table, comparison, taxonomy map. 5. Review topics with an editable review table. 6. Visualize topic distributions with interactive Plotly charts. 7. Download all generated outputs (CSV, JSON). """ import os import json import tempfile import streamlit as st import pandas as pd import plotly.express as px import plotly.graph_objects as go from agent import TopicAgent # --------------------------------------------------------------------------- # HuggingFace Spaces compatibility: use a writable output directory # On HF Spaces the working directory can be read-only, so fall back to /tmp # --------------------------------------------------------------------------- OUTPUT_DIR = "outputs" try: os.makedirs(OUTPUT_DIR, exist_ok=True) # Test write access _test_path = os.path.join(OUTPUT_DIR, ".write_test") with open(_test_path, "w") as _f: _f.write("ok") os.remove(_test_path) except (OSError, PermissionError): OUTPUT_DIR = os.path.join(tempfile.gettempdir(), "topic_modeler_outputs") os.makedirs(OUTPUT_DIR, exist_ok=True) # --------------------------------------------------------------------------- # Page configuration # --------------------------------------------------------------------------- st.set_page_config( page_title="Research Topic Modeler — AI Agent", page_icon="🔬", layout="wide", initial_sidebar_state="expanded", ) # --------------------------------------------------------------------------- # Custom CSS for a polished, professional look with dark-safe text colors # --------------------------------------------------------------------------- st.markdown(""" """, unsafe_allow_html=True) # --------------------------------------------------------------------------- # Header # --------------------------------------------------------------------------- st.markdown("""

🔬 Research Topic Modeler

AI-powered topic modeling agent for research papers — discover, compare, and classify themes across Titles and Abstracts

""", unsafe_allow_html=True) # --------------------------------------------------------------------------- # Sidebar — Configuration # --------------------------------------------------------------------------- with st.sidebar: st.markdown("## ⚙️ Configuration") st.markdown("---") # File upload st.markdown("### 📁 Dataset") uploaded_file = st.file_uploader( "Upload CSV with Title & Abstract columns", type=["csv"], help="The CSV must contain at least 'Title' and 'Abstract' columns.", ) # Or use default dataset use_default = st.checkbox( "Use default dataset (dataset.csv)", value=True if not uploaded_file else False, help="Use the bundled dataset.csv file in the project directory.", ) st.markdown("---") st.markdown("### 🎯 Parameters") min_topics = st.slider( "Minimum Topics", min_value=50, max_value=200, value=100, step=10, help="Minimum number of topics to generate per source (Titles / Abstracts).", ) use_llm = st.checkbox( "🤖 Use LLM for Label Generation (Groq)", value=False, help="Use Groq's LLaMA model to generate contextual topic labels. " "Falls back to keyword heuristic if unchecked.", ) groq_key = os.environ.get("GROQ_API_KEY", "") if use_llm: groq_key = st.text_input( "Groq API Key", value=groq_key, type="password", help="Your Groq API key for LLM label generation.", ) st.markdown("---") st.markdown("### 📋 Pipeline Steps") steps_info = [ "1. Load & validate CSV", "2. Preprocess text (Titles + Abstracts)", "3. Topic modeling — Titles (≥{} topics)".format(min_topics), "4. Topic modeling — Abstracts (≥{} topics)".format(min_topics), "5. Generate human-readable labels", "6. Combine topics table", "7. Compare themes (Title vs Abstract)", "8. Build taxonomy map (MAPPED / NOVEL)", "9. Export outputs (CSV, JSON)", ] for step in steps_info: st.markdown(f'

{step}

', unsafe_allow_html=True) # --------------------------------------------------------------------------- # Main area — Run button and results # --------------------------------------------------------------------------- col_run, col_status = st.columns([2, 3]) with col_run: run_clicked = st.button("🚀 Run Topic Modeling Agent", use_container_width=True, type="primary") with col_status: if "result" in st.session_state and st.session_state.result is not None: res = st.session_state.result if res.status == "success": st.markdown('✓ Pipeline Complete', unsafe_allow_html=True) elif res.status == "failed": st.markdown('✗ Pipeline Failed', unsafe_allow_html=True) else: st.markdown('● Awaiting Input', unsafe_allow_html=True) # --------------------------------------------------------------------------- # Execute pipeline # --------------------------------------------------------------------------- if run_clicked: # Determine CSV path csv_path = None if uploaded_file is not None: # Save uploaded file to a temp location with tempfile.NamedTemporaryFile(delete=False, suffix=".csv", dir=".") as tmp: tmp.write(uploaded_file.getvalue()) csv_path = tmp.name elif use_default: csv_path = "dataset.csv" if not os.path.exists(csv_path): st.error("❌ Default dataset.csv not found in the project directory.") st.stop() else: st.error("❌ Please upload a CSV file or select the default dataset.") st.stop() # Run the agent with st.spinner("🔄 Running the Topic Modeling Agent … this may take a few minutes."): progress = st.progress(0, text="Initializing …") agent = TopicAgent( csv_path=csv_path, output_dir=OUTPUT_DIR, min_topics=min_topics, use_llm_labels=use_llm, groq_api_key=groq_key if use_llm else None, ) # Display step-by-step progress progress.progress(5, text="Step 1/9: Loading CSV …") agent._step_load_csv() progress.progress(10, text="Step 2/9: Preprocessing text …") agent._step_preprocess() progress.progress(20, text="Step 3/9: Topic modeling on Titles …") agent._step_model_titles() progress.progress(45, text="Step 4/9: Topic modeling on Abstracts …") agent._step_model_abstracts() progress.progress(65, text="Step 5/9: Generating topic labels …") agent._step_generate_labels() progress.progress(75, text="Step 6/9: Building combined topics table …") agent._step_combine_topics() progress.progress(80, text="Step 7/9: Comparing themes …") agent._step_compare_themes() progress.progress(90, text="Step 8/9: Building taxonomy map …") agent._step_taxonomy_map() progress.progress(95, text="Step 9/9: Exporting outputs …") agent._step_export() agent._result.status = "success" progress.progress(100, text="✅ Pipeline complete!") st.session_state.result = agent._result # Clean up temp file if uploaded_file is not None and csv_path and os.path.exists(csv_path): try: os.unlink(csv_path) except Exception: pass st.rerun() # --------------------------------------------------------------------------- # Helper: Plotly chart theme (dark background, readable text) # --------------------------------------------------------------------------- PLOTLY_LAYOUT = dict( paper_bgcolor="rgba(26, 26, 46, 0.95)", plot_bgcolor="rgba(22, 33, 62, 0.95)", font=dict(family="Inter, sans-serif", size=13, color="#e0e0e0"), title_font=dict(size=18, color="#ffffff"), legend=dict( font=dict(color="#e0e0e0"), bgcolor="rgba(26, 26, 46, 0.7)", bordercolor="#667eea", borderwidth=1, ), xaxis=dict( gridcolor="rgba(102, 126, 234, 0.15)", zerolinecolor="rgba(102, 126, 234, 0.25)", tickfont=dict(color="#c4b5fd"), title_font=dict(color="#e0e0e0"), ), yaxis=dict( gridcolor="rgba(102, 126, 234, 0.15)", zerolinecolor="rgba(102, 126, 234, 0.25)", tickfont=dict(color="#c4b5fd"), title_font=dict(color="#e0e0e0"), ), margin=dict(l=60, r=30, t=60, b=60), ) # Gradient-like color sequence CHART_COLORS = [ "#667eea", "#764ba2", "#f093fb", "#f5576c", "#4facfe", "#00f2fe", "#43e97b", "#38f9d7", "#fa709a", "#fee140", "#a18cd1", "#fbc2eb", "#ff9a9e", "#fad0c4", "#ffecd2", "#fcb69f", ] # --------------------------------------------------------------------------- # Display results # --------------------------------------------------------------------------- if "result" in st.session_state and st.session_state.result is not None: result = st.session_state.result if result.status == "failed": st.error(f"Pipeline failed with errors: {result.errors}") st.stop() # ---- Summary Statistics ---- st.markdown('

📊 Summary Statistics

', unsafe_allow_html=True) c1, c2, c3, c4, c5 = st.columns(5) with c1: st.markdown(f"""

{len(result.title_topics)}

Title Topics

""", unsafe_allow_html=True) with c2: st.markdown(f"""

{len(result.abstract_topics)}

Abstract Topics

""", unsafe_allow_html=True) with c3: st.markdown(f"""

{len(result.combined_topics)}

Total Topics

""", unsafe_allow_html=True) with c4: mapped_count = result.taxonomy_map.get("metadata", {}).get("mapped_count", 0) st.markdown(f"""

{mapped_count}

Mapped Themes

""", unsafe_allow_html=True) with c5: novel_count = result.taxonomy_map.get("metadata", {}).get("novel_count", 0) st.markdown(f"""

{novel_count}

Novel Themes

""", unsafe_allow_html=True) st.markdown("
", unsafe_allow_html=True) # ---- Tabbed Results ---- tab1, tab2, tab3, tab4, tab5, tab_review, tab_charts = st.tabs([ "📋 Topics Table", "🔬 Title Topics", "📄 Abstract Topics", "⚖️ Theme Comparison", "🗺️ Taxonomy Map", "✏️ Review Table", "📈 Charts", ]) # Tab 1: Combined Topics Table with tab1: st.markdown('

Combined Topics Table

', unsafe_allow_html=True) st.markdown(f"Showing all **{len(result.combined_topics)}** topics from both Titles and Abstracts.") # Filter controls fcol1, fcol2 = st.columns(2) with fcol1: source_filter = st.multiselect( "Filter by Source", options=result.combined_topics["source"].unique().tolist(), default=result.combined_topics["source"].unique().tolist(), ) with fcol2: search_term = st.text_input("🔍 Search keywords", "") display_df = result.combined_topics[result.combined_topics["source"].isin(source_filter)] if search_term: mask = display_df["keywords"].str.contains(search_term, case=False, na=False) mask |= display_df["label"].str.contains(search_term, case=False, na=False) display_df = display_df[mask] st.dataframe( display_df, use_container_width=True, height=500, column_config={ "topic_id": st.column_config.NumberColumn("Topic ID", width="small"), "keywords": st.column_config.TextColumn("Keywords", width="large"), "label": st.column_config.TextColumn("Label", width="medium"), "source": st.column_config.TextColumn("Source", width="small"), }, ) # Tab 2: Title Topics with tab2: st.markdown('

Title Topics

', unsafe_allow_html=True) st.markdown(f"**{len(result.title_topics)}** topics discovered from paper titles.") st.dataframe(result.title_topics, use_container_width=True, height=500) # Tab 3: Abstract Topics with tab3: st.markdown('

Abstract Topics

', unsafe_allow_html=True) st.markdown(f"**{len(result.abstract_topics)}** topics discovered from paper abstracts.") st.dataframe(result.abstract_topics, use_container_width=True, height=500) # Tab 4: Theme Comparison with tab4: st.markdown('

Theme Comparison: Titles vs Abstracts

', unsafe_allow_html=True) if not result.comparison.empty: # Alignment distribution align_counts = result.comparison["alignment"].value_counts() acol1, acol2, acol3, acol4 = st.columns(4) for col, alignment in zip( [acol1, acol2, acol3, acol4], ["Strong", "Moderate", "Weak", "No Match"], ): with col: count = align_counts.get(alignment, 0) st.metric(label=f"{alignment} Alignment", value=count) st.markdown("
", unsafe_allow_html=True) # Filter by alignment alignment_filter = st.multiselect( "Filter by Alignment", options=["Strong", "Moderate", "Weak", "No Match"], default=["Strong", "Moderate", "Weak", "No Match"], ) filtered_comp = result.comparison[result.comparison["alignment"].isin(alignment_filter)] st.dataframe( filtered_comp, use_container_width=True, height=500, column_config={ "similarity": st.column_config.ProgressColumn( "Similarity", min_value=0, max_value=1, format="%.2f", ), }, ) else: st.info("No comparison data available.") # Tab 5: Taxonomy Map with tab5: st.markdown('

Taxonomy Map

', unsafe_allow_html=True) taxonomy = result.taxonomy_map meta = taxonomy.get("metadata", {}) st.markdown(f"""

Classification Summary:
Total Topics: {meta.get('total_topics', 0)} | MAPPED: {meta.get('mapped_count', 0)} | NOVEL: {meta.get('novel_count', 0)} | Threshold: {meta.get('threshold', 0.15)}

""", unsafe_allow_html=True) tax_tab1, tax_tab2 = st.tabs(["✅ Mapped Themes", "🆕 Novel Themes"]) with tax_tab1: mapped_list = taxonomy.get("mapped", []) if mapped_list: mapped_df = pd.DataFrame(mapped_list) st.dataframe( mapped_df, use_container_width=True, height=400, column_config={ "score": st.column_config.ProgressColumn( "Match Score", min_value=0, max_value=1, format="%.3f", ), }, ) else: st.info("No mapped themes found.") with tax_tab2: novel_list = taxonomy.get("novel", []) if novel_list: novel_df = pd.DataFrame(novel_list) st.dataframe( novel_df, use_container_width=True, height=400, column_config={ "score": st.column_config.ProgressColumn( "Match Score", min_value=0, max_value=1, format="%.3f", ), }, ) else: st.info("No novel themes found.") # ================================================================== # Tab 6: Editable Review Table # ================================================================== with tab_review: st.markdown("""

✏️ Topic Review Table

Review, approve, rename, and annotate each topic. Changes are saved to outputs/review_table.csv.

""", unsafe_allow_html=True) # Build review dataframe from combined topics # Load existing review table if available to preserve edits review_csv_path = os.path.join(OUTPUT_DIR, "review_table.csv") if "review_df" not in st.session_state: if os.path.exists(review_csv_path): # Load previously saved review table existing_review = pd.read_csv(review_csv_path) # Merge with current topics to ensure all topics are represented current_ids = set(result.combined_topics["topic_id"].tolist()) existing_ids = set(existing_review["topic_id"].tolist()) if "topic_id" in existing_review.columns else set() if current_ids == existing_ids or existing_ids.issuperset(current_ids): st.session_state.review_df = existing_review else: # Rebuild from current topics, but preserve existing edits review_data = [] for _, row in result.combined_topics.iterrows(): review_data.append({ "topic_id": int(row["topic_id"]), "label": row.get("label", ""), "keywords": row.get("keywords", ""), "source": row.get("source", ""), "approve": False, "rename_to": "", "reasoning": "", }) new_review_df = pd.DataFrame(review_data) # Merge existing edits if not existing_review.empty and "topic_id" in existing_review.columns: for _, erow in existing_review.iterrows(): mask = new_review_df["topic_id"] == erow["topic_id"] if mask.any(): if "approve" in erow: new_review_df.loc[mask, "approve"] = erow["approve"] if "rename_to" in erow and pd.notna(erow["rename_to"]): new_review_df.loc[mask, "rename_to"] = erow["rename_to"] if "reasoning" in erow and pd.notna(erow["reasoning"]): new_review_df.loc[mask, "reasoning"] = erow["reasoning"] st.session_state.review_df = new_review_df else: # Build fresh review table review_data = [] for _, row in result.combined_topics.iterrows(): review_data.append({ "topic_id": int(row["topic_id"]), "label": row.get("label", ""), "keywords": row.get("keywords", ""), "source": row.get("source", ""), "approve": False, "rename_to": "", "reasoning": "", }) st.session_state.review_df = pd.DataFrame(review_data) # Filter controls for review table rv_col1, rv_col2, rv_col3 = st.columns(3) with rv_col1: review_source_filter = st.multiselect( "Filter by Source", options=st.session_state.review_df["source"].unique().tolist(), default=st.session_state.review_df["source"].unique().tolist(), key="review_source_filter", ) with rv_col2: review_search = st.text_input("🔍 Search in review table", "", key="review_search") with rv_col3: review_approval_filter = st.selectbox( "Show", options=["All Topics", "Approved Only", "Not Approved"], index=0, key="review_approval_filter", ) # Apply filters filtered_review = st.session_state.review_df[ st.session_state.review_df["source"].isin(review_source_filter) ] if review_search: search_mask = ( filtered_review["keywords"].str.contains(review_search, case=False, na=False) | filtered_review["label"].str.contains(review_search, case=False, na=False) ) filtered_review = filtered_review[search_mask] if review_approval_filter == "Approved Only": filtered_review = filtered_review[filtered_review["approve"] == True] elif review_approval_filter == "Not Approved": filtered_review = filtered_review[filtered_review["approve"] == False] # Editable data editor edited_df = st.data_editor( filtered_review, use_container_width=True, height=500, num_rows="fixed", key="review_editor", column_config={ "topic_id": st.column_config.NumberColumn( "Topic ID", width="small", disabled=True ), "label": st.column_config.TextColumn( "Label", width="medium", ), "keywords": st.column_config.TextColumn( "Keywords", width="large", disabled=True, ), "source": st.column_config.TextColumn( "Source", width="small", disabled=True, ), "approve": st.column_config.CheckboxColumn( "✅ Approve", width="small", default=False, ), "rename_to": st.column_config.TextColumn( "Rename To", width="medium", ), "reasoning": st.column_config.TextColumn( "Reasoning / Notes", width="large", ), }, column_order=["topic_id", "label", "keywords", "approve", "rename_to", "reasoning", "source"], ) # Update session state with edits if edited_df is not None: # Merge edits back into the full review dataframe for idx, erow in edited_df.iterrows(): mask = st.session_state.review_df.index == idx if mask.any(): for col in ["label", "approve", "rename_to", "reasoning"]: if col in erow: st.session_state.review_df.loc[mask, col] = erow[col] # Save button sv_col1, sv_col2, sv_col3 = st.columns([1, 1, 2]) with sv_col1: if st.button("💾 Save Review Table", use_container_width=True, type="primary"): os.makedirs(OUTPUT_DIR, exist_ok=True) st.session_state.review_df.to_csv(review_csv_path, index=False) st.markdown( '

✅ Review table saved to outputs/review_table.csv

', unsafe_allow_html=True, ) with sv_col2: approved_count = int(st.session_state.review_df["approve"].sum()) if "approve" in st.session_state.review_df.columns else 0 total_count = len(st.session_state.review_df) st.markdown(f"""

{approved_count}/{total_count}

Topics Approved

""", unsafe_allow_html=True) # ================================================================== # Tab 7: Charts # ================================================================== with tab_charts: st.markdown('

📈 Topic Visualizations

', unsafe_allow_html=True) # ----------------------------------------------------------- # Chart 1: Topic Frequency by Source # ----------------------------------------------------------- st.markdown("#### 📊 Topic Frequency by Source") st.caption("Number of topics discovered from each source (Titles vs Abstracts).") source_counts = result.combined_topics["source"].value_counts().reset_index() source_counts.columns = ["Source", "Count"] fig1 = px.bar( source_counts, x="Source", y="Count", color="Source", color_discrete_sequence=["#667eea", "#764ba2"], text="Count", ) fig1.update_traces( textposition="outside", textfont=dict(color="#e0e0e0", size=14, family="Inter"), marker=dict( line=dict(width=0), ), ) fig1.update_layout( **PLOTLY_LAYOUT, title="Topic Count by Source", xaxis_title="Source", yaxis_title="Number of Topics", showlegend=False, height=420, ) st.plotly_chart(fig1, use_container_width=True) st.markdown("---") # ----------------------------------------------------------- # Chart 2: Top Keywords Across All Topics # ----------------------------------------------------------- st.markdown("#### 🔤 Top Keywords Across All Topics") st.caption("Most frequently occurring keywords across all discovered topics.") # Extract all keywords, count frequencies all_keywords = [] for kw_str in result.combined_topics["keywords"].dropna(): for kw in kw_str.split(","): kw_clean = kw.strip().lower() if kw_clean and len(kw_clean) > 2: all_keywords.append(kw_clean) kw_counts = pd.Series(all_keywords).value_counts().head(25).reset_index() kw_counts.columns = ["Keyword", "Frequency"] fig2 = px.bar( kw_counts, x="Frequency", y="Keyword", orientation="h", color="Frequency", color_continuous_scale=["#302b63", "#667eea", "#f093fb", "#f5576c"], ) fig2.update_traces( marker=dict(line=dict(width=0)), ) fig2.update_layout( **PLOTLY_LAYOUT, title="Top 25 Keywords by Frequency", xaxis_title="Frequency (across all topics)", yaxis_title="", height=700, coloraxis_colorbar=dict( title="Freq", tickfont=dict(color="#c4b5fd"), title_font=dict(color="#e0e0e0"), ), ) # Override yaxis separately to avoid duplicate keyword with PLOTLY_LAYOUT fig2.update_layout( yaxis=dict( autorange="reversed", gridcolor="rgba(102, 126, 234, 0.1)", tickfont=dict(color="#c4b5fd", size=12), ), ) st.plotly_chart(fig2, use_container_width=True) st.markdown("---") # ----------------------------------------------------------- # Chart 3: Taxonomy Distribution (Mapped vs Novel) # ----------------------------------------------------------- st.markdown("#### 🧬 Taxonomy Classification Distribution") st.caption("How topics are classified against the known research taxonomy.") tax_meta = result.taxonomy_map.get("metadata", {}) tax_data = pd.DataFrame({ "Classification": ["MAPPED", "NOVEL"], "Count": [tax_meta.get("mapped_count", 0), tax_meta.get("novel_count", 0)], }) chart3_col1, chart3_col2 = st.columns(2) with chart3_col1: fig3a = px.pie( tax_data, values="Count", names="Classification", color="Classification", color_discrete_map={ "MAPPED": "#38ef7d", "NOVEL": "#f7b733", }, hole=0.55, ) fig3a.update_traces( textfont=dict(color="#ffffff", size=14), textinfo="percent+label", marker=dict(line=dict(color="#1a1a2e", width=3)), ) fig3a.update_layout( paper_bgcolor="rgba(26, 26, 46, 0.95)", plot_bgcolor="rgba(22, 33, 62, 0.95)", font=dict(family="Inter, sans-serif", size=13, color="#e0e0e0"), title=dict(text="Mapped vs Novel", font=dict(size=16, color="#ffffff")), legend=dict(font=dict(color="#e0e0e0")), height=380, margin=dict(l=20, r=20, t=50, b=20), ) st.plotly_chart(fig3a, use_container_width=True) with chart3_col2: fig3b = px.bar( tax_data, x="Classification", y="Count", color="Classification", color_discrete_map={ "MAPPED": "#38ef7d", "NOVEL": "#f7b733", }, text="Count", ) fig3b.update_traces( textposition="outside", textfont=dict(color="#e0e0e0", size=16, family="Inter"), marker=dict(line=dict(width=0)), ) fig3b.update_layout( **PLOTLY_LAYOUT, title="Classification Count", xaxis_title="", yaxis_title="Number of Topics", showlegend=False, height=380, ) st.plotly_chart(fig3b, use_container_width=True) st.markdown("---") # ----------------------------------------------------------- # Chart 4: Alignment Distribution (from comparisons) # ----------------------------------------------------------- if not result.comparison.empty: st.markdown("#### ⚖️ Theme Alignment Distribution") st.caption("Distribution of alignment strength between Title and Abstract topics.") alignment_data = result.comparison["alignment"].value_counts().reset_index() alignment_data.columns = ["Alignment", "Count"] # Define order and colors align_order = ["Strong", "Moderate", "Weak", "No Match"] align_colors = { "Strong": "#38ef7d", "Moderate": "#4facfe", "Weak": "#f7971e", "No Match": "#f5576c", } fig4 = px.bar( alignment_data, x="Alignment", y="Count", color="Alignment", color_discrete_map=align_colors, text="Count", category_orders={"Alignment": align_order}, ) fig4.update_traces( textposition="outside", textfont=dict(color="#e0e0e0", size=14, family="Inter"), marker=dict(line=dict(width=0)), ) fig4.update_layout( **PLOTLY_LAYOUT, title="Title ↔ Abstract Alignment Distribution", xaxis_title="Alignment Level", yaxis_title="Number of Topic Pairs", showlegend=False, height=420, ) st.plotly_chart(fig4, use_container_width=True) st.markdown("---") # ----------------------------------------------------------- # Chart 5: Similarity Score Histogram # ----------------------------------------------------------- st.markdown("#### 📐 Similarity Score Distribution") st.caption("Distribution of Jaccard similarity scores between matched Title and Abstract topics.") fig5 = px.histogram( result.comparison, x="similarity", nbins=30, color_discrete_sequence=["#667eea"], marginal="box", ) fig5.update_traces( marker=dict( line=dict(width=1, color="#b8b5ff"), ), selector=dict(type="histogram"), ) fig5.update_layout( **PLOTLY_LAYOUT, title="Similarity Score Histogram", xaxis_title="Jaccard Similarity Score", yaxis_title="Count", height=420, bargap=0.05, ) st.plotly_chart(fig5, use_container_width=True) # ---- Downloads Section ---- st.markdown('

📥 Download Outputs

', unsafe_allow_html=True) dcol1, dcol2, dcol3, dcol4 = st.columns(4) with dcol1: csv_data = result.combined_topics.to_csv(index=False) st.download_button( "⬇️ Topics Table (CSV)", data=csv_data, file_name="topics_table.csv", mime="text/csv", use_container_width=True, ) with dcol2: comp_data = result.comparison.to_csv(index=False) st.download_button( "⬇️ Comparison (CSV)", data=comp_data, file_name="comparison.csv", mime="text/csv", use_container_width=True, ) with dcol3: json_data = json.dumps(result.taxonomy_map, indent=2, ensure_ascii=False) st.download_button( "⬇️ Taxonomy Map (JSON)", data=json_data, file_name="taxonomy_map.json", mime="application/json", use_container_width=True, ) with dcol4: # Download review table if it exists review_path = os.path.join(OUTPUT_DIR, "review_table.csv") if os.path.exists(review_path): with open(review_path, "r") as f: review_data = f.read() st.download_button( "⬇️ Review Table (CSV)", data=review_data, file_name="review_table.csv", mime="text/csv", use_container_width=True, ) else: st.download_button( "⬇️ Review Table (CSV)", data="Not saved yet. Go to Review Table tab and click Save.", file_name="review_table.csv", mime="text/csv", use_container_width=True, disabled=True, ) # ---- Auto-save comparison.csv and taxonomy_map.json to outputs ---- os.makedirs(OUTPUT_DIR, exist_ok=True) result.comparison.to_csv(os.path.join(OUTPUT_DIR, "comparison.csv"), index=False) with open(os.path.join(OUTPUT_DIR, "taxonomy_map.json"), "w", encoding="utf-8") as f: json.dump(result.taxonomy_map, f, indent=2, ensure_ascii=False) # ---- Pipeline Log ---- with st.expander("📜 Pipeline Execution Log"): st.markdown(f"**Status:** `{result.status}`") st.markdown(f"**Steps Completed:** {len(result.steps_completed)}/9") for i, step in enumerate(result.steps_completed, 1): st.markdown(f" ✅ Step {i}: `{step}`") if result.errors: st.markdown("**Errors:**") for err in result.errors: st.error(err) st.markdown("**Exported Files:**") for name, path in result.exported_files.items(): st.markdown(f" 📄 `{name}` → `{path}`") else: # ---- Welcome / instructions when no results ---- st.markdown("""

👋 Welcome!

This application uses an AI agent to perform comprehensive topic modeling on research papers.

How to use:
1️⃣ Upload a CSV file with Title and Abstract columns (or use the default dataset).
2️⃣ Configure the minimum number of topics and label generation method in the sidebar.
3️⃣ Click "🚀 Run Topic Modeling Agent" to start the analysis.
4️⃣ Explore topics, comparisons, and taxonomy classification in the results tabs.
5️⃣ Review and annotate topics in the ✏️ Review Table tab.
6️⃣ View interactive charts in the 📈 Charts tab.
7️⃣ Download all outputs as CSV and JSON files.

""", unsafe_allow_html=True) st.markdown("
", unsafe_allow_html=True) # Show a preview if default dataset exists if os.path.exists("dataset.csv"): with st.expander("👀 Preview Default Dataset", expanded=False): try: preview_df = pd.read_csv("dataset.csv", nrows=10) st.markdown(f"**Columns:** {', '.join(preview_df.columns.tolist())}") if "Title" in preview_df.columns: st.dataframe(preview_df[["Title", "Abstract"]].head(10) if "Abstract" in preview_df.columns else preview_df[["Title"]].head(10), use_container_width=True) else: st.dataframe(preview_df.head(10), use_container_width=True) except Exception as e: st.warning(f"Could not preview dataset: {e}")