| """ |
| web_app.py — Complaint Auto-Routing System · Gradio Web Interface |
| ──────────────────────────────────────────────────────────────── |
| |
| Run: |
| python app/web_app.py |
| # → opens at http://localhost:7860 |
| |
| Install Gradio: |
| pip install gradio |
| |
| No external API keys required. |
| """ |
|
|
| import os |
| import sys |
| import glob |
| import json |
| import textwrap |
|
|
| |
| if sys.platform == "win32": |
| |
| winget_packages = os.path.expandvars(r"%LOCALAPPDATA%\Microsoft\WinGet\Packages") |
| if os.path.exists(winget_packages): |
| |
| ffmpeg_bins = glob.glob(os.path.join(winget_packages, "Gyan.FFmpeg*", "**", "bin"), recursive=True) |
| if ffmpeg_bins: |
| ffmpeg_path = ffmpeg_bins[0] |
| if ffmpeg_path not in os.environ["PATH"]: |
| os.environ["PATH"] = ffmpeg_path + os.pathsep + os.environ["PATH"] |
| print(f"[Startup] Automatically resolved system FFmpeg path at: {ffmpeg_path}") |
|
|
| sys.path.insert(0, os.path.dirname(os.path.dirname(__file__))) |
|
|
| from inference.engine import ComplaintRoutingEngine, SAVE_DIR |
|
|
| |
| def ensure_models_trained(): |
| required_files = [ |
| "embedding_engine.pkl", |
| "officer_classifier.pkl", |
| "priority_classifier.pkl", |
| "eta_regressor.pkl", |
| "label_encoders.pkl", |
| "vector_store.pkl" |
| ] |
| all_exist = all(os.path.exists(os.path.join(SAVE_DIR, f)) for f in required_files) |
| if not all_exist: |
| print("[Startup] Missing trained models. Initiating automatic data generation and training...") |
| |
| |
| data_path = os.path.join(os.path.dirname(os.path.dirname(__file__)), "data", "synthetic_complaints.csv") |
| if not os.path.exists(data_path): |
| print("[Startup] Generating synthetic complaints dataset...") |
| from data.generate_data import generate_complaints |
| df = generate_complaints(n_per_officer=100) |
| os.makedirs(os.path.dirname(data_path), exist_ok=True) |
| df.to_csv(data_path, index=False) |
| print(f"[Startup] Generated {len(df)} complaints.") |
| |
| |
| print("[Startup] Training models offline...") |
| from models.train import main as train_main |
| train_main() |
| print("[Startup] Model training complete.") |
|
|
| ensure_models_trained() |
| engine = ComplaintRoutingEngine().load(SAVE_DIR) |
|
|
| |
| PRIORITY_BADGE = { |
| "High": '<span style="display:inline-block;white-space:nowrap;min-width:70px;text-align:center;background:#ef4444;color:#fff;padding:4px 10px;border-radius:4px;font-weight:700;font-size:12px">High</span>', |
| "Medium": '<span style="display:inline-block;white-space:nowrap;min-width:70px;text-align:center;background:#f59e0b;color:#fff;padding:4px 10px;border-radius:4px;font-weight:700;font-size:12px">Med</span>', |
| "Low": '<span style="display:inline-block;white-space:nowrap;min-width:70px;text-align:center;background:#22c55e;color:#fff;padding:4px 10px;border-radius:4px;font-weight:700;font-size:12px">Low</span>', |
| } |
|
|
| DEPT_ICON = { |
| "Infrastructure & Roads": "🛣️", |
| "Water & Sanitation": "💧", |
| "Electricity & Utilities": "⚡", |
| "Public Safety & Security": "🛡️", |
| "Health & Environment": "🌿", |
| "Land & Property": "🏠", |
| "Transport & Traffic": "🚌", |
| "Administrative Services": "📋", |
| } |
|
|
|
|
| def build_output_html(result: dict) -> str: |
| o = result["officer"] |
| p = result["priority"] |
| eta = result["eta_days"] |
| sim = result.get("similar_complaints", []) |
| icon = DEPT_ICON.get(o["department"], "🏛️") |
| badge = PRIORITY_BADGE.get(p["level"], p["level"]) |
|
|
| |
| sim_rows = "" |
| for s in sim: |
| snip = textwrap.shorten(s["text_snippet"].replace("…", ""), width=80) |
| sb = PRIORITY_BADGE.get(s["priority"], s["priority"]) |
| sim_rows += f""" |
| <tr> |
| <td style="padding:8px 8px;font-size:12px;color:#475569">{s['complaint_id']}</td> |
| <td style="padding:8px 8px;font-size:12px;color:#1e293b;line-height:1.4">{snip}</td> |
| <td style="padding:8px 8px;text-align:center">{sb}</td> |
| <td style="padding:8px 8px;text-align:center;font-size:12px;color:#1e293b;font-weight:600">{s['eta_days']}d</td> |
| <td style="padding:8px 8px;text-align:center;font-size:12px;color:#6366f1;font-weight:600">{s['similarity_score']:.3f}</td> |
| </tr>""" |
|
|
| |
| transcription_section = "" |
| if result.get("source_text"): |
| transcription_section = f""" |
| <div style="background:#f8fafc;border:1px solid #e2e8f0;border-radius:10px;padding:14px;margin-bottom:16px"> |
| <div style="font-size:11px;color:#64748b;font-weight:600;text-transform:uppercase;letter-spacing:.5px;margin-bottom:6px">Transcribed Text</div> |
| <div style="font-size:13px;color:#1e293b;line-height:1.5;font-style:italic">"{result['source_text']}"</div> |
| </div>""" |
|
|
| html = f""" |
| <div style="font-family:Inter,system-ui,sans-serif;max-width:700px"> |
| |
| {transcription_section} |
| |
| <div style="display:grid;grid-template-columns:1fr 1fr 1fr;gap:12px;margin-bottom:16px"> |
| <div style="background:#f0f9ff;border:1px solid #bae6fd;border-radius:10px;padding:14px"> |
| <div style="font-size:11px;color:#0369a1;font-weight:600;text-transform:uppercase;letter-spacing:.5px">Assigned Officer</div> |
| <div style="font-size:20px;margin:4px 0"></div> |
| <div style="font-weight:700;font-size:15px;color:#0c4a6e">{o['name']}</div> |
| <div style="font-size:12px;color:#0369a1;margin-top:2px">{o['department']}</div> |
| <div style="font-size:11px;color:#94a3b8;margin-top:4px">{o['id']} · {o['confidence']}% conf.</div> |
| </div> |
| |
| <div style="background:#fefce8;border:1px solid #fde68a;border-radius:10px;padding:14px"> |
| <div style="font-size:11px;color:#92400e;font-weight:600;text-transform:uppercase;letter-spacing:.5px">Priority</div> |
| <div style="font-size:20px;margin:4px 0"></div> |
| <div style="margin-top:4px">{badge}</div> |
| <div style="font-size:11px;color:#94a3b8;margin-top:6px">{p['confidence']}% confidence</div> |
| </div> |
| |
| <div style="background:#f0fdf4;border:1px solid #bbf7d0;border-radius:10px;padding:14px"> |
| <div style="font-size:11px;color:#166534;font-weight:600;text-transform:uppercase;letter-spacing:.5px">Est. Resolution</div> |
| <div style="font-size:20px;margin:4px 0"></div> |
| <div style="font-weight:700;font-size:22px;color:#14532d">{eta}</div> |
| <div style="font-size:12px;color:#166534">day(s)</div> |
| </div> |
| </div> |
| |
| <div style="background:#fafafa;border:1px solid #e2e8f0;border-radius:10px;padding:14px"> |
| <div style="font-size:12px;font-weight:700;color:#1e293b;margin-bottom:8px">Similar Past Complaints (Top {len(sim)})</div> |
| <table style="width:100%;border-collapse:collapse"> |
| <thead> |
| <tr style="border-bottom:1px solid #e2e8f0"> |
| <th style="text-align:left;font-size:11px;color:#475569;padding:6px 8px;width:70px">ID</th> |
| <th style="text-align:left;font-size:11px;color:#475569;padding:6px 8px">Snippet</th> |
| <th style="font-size:11px;color:#475569;padding:6px 8px;width:105px;text-align:center">Priority</th> |
| <th style="font-size:11px;color:#475569;padding:6px 8px;width:60px;text-align:center">ETA</th> |
| <th style="font-size:11px;color:#475569;padding:6px 8px;width:60px;text-align:center">Score</th> |
| </tr> |
| </thead> |
| <tbody>{sim_rows}</tbody> |
| </table> |
| </div> |
| |
| </div> |
| """ |
| return html |
|
|
|
|
| def route_text_complaint(text: str, top_k: int) -> tuple: |
| if not text.strip(): |
| return "<p style='color:red'>Please enter complaint text.</p>", "" |
| result = engine.predict(text.strip(), top_k_similar=int(top_k)) |
| html = build_output_html(result) |
| raw = json.dumps(result, indent=2, ensure_ascii=False) |
| return html, raw |
|
|
|
|
| def route_audio_complaint(audio_file, top_k: int) -> tuple: |
| if audio_file is None: |
| return "<p style='color:red'>Please upload an audio file.</p>", "" |
| try: |
| result = engine.process(audio_path=audio_file, top_k=int(top_k)) |
| except ImportError as e: |
| return f"<p style='color:orange;font-weight:600'>Dependency Error: {e}</p>", "" |
| except Exception as e: |
| err_msg = str(e) |
| if "ffmpeg" in err_msg.lower() or "winerror 2" in err_msg.lower(): |
| return ( |
| "<div style='background:#fffbeb;border:1px solid #fef3c7;padding:16px;border-radius:8px;color:#b45309;line-height:1.5'>" |
| "<strong>System Configuration Error: FFmpeg not detected!</strong><br/>" |
| "Whisper requires FFmpeg to process and decode audio uploads.<br/><br/>" |
| "<strong>To fix this on Windows:</strong><br/>" |
| "1. Open PowerShell as Administrator and run: <code>winget install Gyan.FFmpeg</code><br/>" |
| "2. <strong>Crucial:</strong> Close and restart your IDE (VS Code), terminal, or command prompt so Windows reloads the new PATH system variable.<br/>" |
| "3. Restart the web app and try again." |
| "</div>", |
| f"Error details: {err_msg}" |
| ) |
| return f"<div style='color:red;padding:12px;border:1px solid #fecaca;background:#fef2f2;border-radius:8px;line-height:1.5'><strong>Error processing audio:</strong> {err_msg}</div>", f"Error details: {err_msg}" |
| html = build_output_html(result) |
| raw = json.dumps(result, indent=2, ensure_ascii=False) |
| return html, raw |
|
|
|
|
| def route_video_complaint(video_file, top_k: int) -> tuple: |
| if video_file is None: |
| return "<p style='color:red'>Please upload a video file.</p>", "" |
| try: |
| result = engine.process(video_path=video_file, top_k=int(top_k)) |
| except ImportError as e: |
| return f"<p style='color:orange;font-weight:600'>Dependency Error: {e}</p>", "" |
| except Exception as e: |
| err_msg = str(e) |
| if "ffmpeg" in err_msg.lower() or "winerror 2" in err_msg.lower(): |
| return ( |
| "<div style='background:#fffbeb;border:1px solid #fef3c7;padding:16px;border-radius:8px;color:#b45309;line-height:1.5'>" |
| "<strong>System Configuration Error: FFmpeg not detected!</strong><br/>" |
| "Whisper requires FFmpeg to extract audio from video uploads.<br/><br/>" |
| "<strong>To fix this on Windows:</strong><br/>" |
| "1. Open PowerShell as Administrator and run: <code>winget install Gyan.FFmpeg</code><br/>" |
| "2. <strong>Crucial:</strong> Close and restart your IDE (VS Code), terminal, or command prompt so Windows reloads the new PATH system variable.<br/>" |
| "3. Restart the web app and try again." |
| "</div>", |
| f"Error details: {err_msg}" |
| ) |
| return f"<div style='color:red;padding:12px;border:1px solid #fecaca;background:#fef2f2;border-radius:8px;line-height:1.5'><strong>Error processing video:</strong> {err_msg}</div>", f"Error details: {err_msg}" |
| html = build_output_html(result) |
| raw = json.dumps(result, indent=2, ensure_ascii=False) |
| return html, raw |
|
|
|
|
| def create_app(): |
| try: |
| import gradio as gr |
| except ImportError: |
| raise ImportError("pip install gradio # then retry") |
|
|
| EXAMPLE_COMPLAINTS = [ |
| ["There is a massive pothole on Brigade Road near the hospital causing accidents. URGENT! People are in immediate danger.", 5], |
| ["My ration card application has been pending for 45 days. Not urgent, but the matter needs attention when convenient.", 5], |
| ["Sewage water overflowing near Central Park. This is a serious problem affecting daily life. Requesting action at the earliest.", 5], |
| ["This is a very serious problem. A live electric wire has fallen near the school. People are in immediate danger. Urgent action needed!", 5], |
| ["Bus route 42 from East Colony has been suspended for 10 days without notice. Multiple families are affected.", 5], |
| ["Illegal construction is happening on government land near the Railway Station. Not urgent but needs attention.", 5], |
| ] |
|
|
| with gr.Blocks( |
| title="Complaint Auto-Routing System", |
| theme=gr.themes.Soft(), |
| css=""" |
| * { font-family: 'Inter', 'Segoe UI', system-ui, sans-serif !important; } |
| .gradio-container { max-width: 900px !important; margin: 0 auto; } |
| footer { display: none; } |
| """, |
| ) as demo: |
|
|
| gr.Markdown(""" |
| # Complaint Auto-Routing System |
| |
| AI/ML system that automatically routes complaints to the right officer, predicts priority and resolution time, and retrieves similar past complaints — **fully offline, no external APIs**. |
| """) |
|
|
| with gr.Tabs(): |
|
|
| |
| with gr.Tab("Text Complaint"): |
| with gr.Row(): |
| text_input = gr.Textbox( |
| label="Complaint Text", |
| placeholder="Describe your complaint here in English…", |
| lines=4, |
| ) |
| top_k_text = gr.Slider(1, 10, value=5, step=1, label="Similar complaints") |
| text_btn = gr.Button("Route Complaint", variant="primary") |
| text_output = gr.HTML(label="Routing Result") |
|
|
| with gr.Accordion("Show raw JSON", open=False): |
| text_json = gr.Code(label="JSON", language="json") |
|
|
| gr.Markdown("<br>💡 **Tip:** Click on any of the examples below to instantly test the routing system!") |
|
|
| gr.Examples( |
| examples=EXAMPLE_COMPLAINTS, |
| inputs=[text_input, top_k_text], |
| ) |
|
|
| text_btn.click( |
| fn=route_text_complaint, |
| inputs=[text_input, top_k_text], |
| outputs=[text_output, text_json], |
| ) |
|
|
| |
| with gr.Tab("Audio Complaint"): |
| gr.Markdown(""" |
| Upload an audio recording of the complaint in English. |
| **Requires:** `pip install openai-whisper` (local model, English) |
| """) |
| audio_input = gr.Audio(type="filepath", label="Audio File (.wav, .mp3, .m4a)") |
| top_k_audio = gr.Slider(1, 10, value=5, step=1, label="Similar complaints") |
| audio_btn = gr.Button("Transcribe & Route", variant="primary") |
| audio_out = gr.HTML(label="Result") |
| audio_json = gr.Code(label="JSON", language="json") |
|
|
| gr.Markdown("<br>💡 **Tip:** Click the sample audio file below to test the transcription and routing without needing your own file!") |
|
|
| gr.Examples( |
| examples=[["data/sample_audio.mp3", 5]], |
| inputs=[audio_input, top_k_audio], |
| ) |
|
|
| audio_btn.click( |
| fn=route_audio_complaint, |
| inputs=[audio_input, top_k_audio], |
| outputs=[audio_out, audio_json], |
| ) |
|
|
| |
| with gr.Tab("Video Complaint"): |
| gr.Markdown(""" |
| Upload a video of the complainant speaking. |
| **Requires:** `pip install openai-whisper` + `ffmpeg` installed system-wide. |
| """) |
| video_input = gr.Video(label="Video File (.mp4, .mkv, .avi)") |
| top_k_video = gr.Slider(1, 10, value=5, step=1, label="Similar complaints") |
| video_btn = gr.Button("Extract Audio & Route", variant="primary") |
| video_out = gr.HTML(label="Result") |
| video_json = gr.Code(label="JSON", language="json") |
| video_btn.click( |
| fn=route_video_complaint, |
| inputs=[video_input, top_k_video], |
| outputs=[video_out, video_json], |
| ) |
|
|
| |
| with gr.Tab("About"): |
| gr.Markdown(""" |
| ## Architecture |
| |
| | Component | Model | Notes | |
| |-----------|-------|-------| |
| | **Embeddings** | TF-IDF + SVD (256-dim) | Offline baseline; swap for `all-MiniLM-L6-v2` (English SentenceTransformer) | |
| | **Officer Routing** | SVM (RBF kernel) | 8-class, probability calibrated | |
| | **Priority** | Random Forest | High / Medium / Low | |
| | **ETA Prediction** | Gradient Boosting Regressor | MAE ≈ 5–8 days | |
| | **Similarity Search** | Cosine over NumPy matrix | FAISS drop-in available | |
| | **Audio/Video** | Whisper (local) | English, fully offline | |
| |
| ## Officers |
| | ID | Name | Department | |
| |----|------|-----------| |
| | OFF001 | Rahul Sharma | Infrastructure & Roads | |
| | OFF002 | Priya Mehta | Water & Sanitation | |
| | OFF003 | Amit Verma | Electricity & Utilities | |
| | OFF004 | Sunita Patel | Public Safety & Security | |
| | OFF005 | Vijay Kumar | Health & Environment | |
| | OFF006 | Anjali Singh | Land & Property | |
| | OFF007 | Ravi Nair | Transport & Traffic | |
| | OFF008 | Meena Reddy | Administrative Services | |
| |
| ## No External APIs |
| All inference happens locally. Models are trained from scratch on synthetic data. |
| For production, replace synthetic data with real complaint records. |
| """) |
|
|
| return demo |
|
|
|
|
| if __name__ == "__main__": |
| app = create_app() |
| app.launch(server_name="0.0.0.0", server_port=7860, share=False) |
|
|
