Spaces:
Running on Zero
Running on Zero
| import spaces | |
| import json | |
| import gradio as gr | |
| from inference import ( | |
| cnn_traffic, | |
| lstm_energy, | |
| gnn_fraud, | |
| transformer_reports, | |
| autoencoder_leaks, | |
| ) | |
| def cnn_fn(image): | |
| if image is None: | |
| return {"error": "No image provided"} | |
| result = cnn_traffic.predict(image) | |
| return {item["label"]: item["confidence"] for item in result["top_k"]} | |
| # ---------- Module 02: LSTM ---------- | |
| def lstm_fn(history_text): | |
| try: | |
| values = [float(x.strip()) for x in history_text.replace("\n", ",").split(",") if x.strip()] | |
| result = lstm_energy.forecast(values) | |
| return f"Predicted next-day consumption: {result['forecast_kwh']:.2f} kWh" | |
| except Exception as e: | |
| return f"❌ Error: {e}" | |
| # ---------- Module 03: GNN ---------- | |
| def gnn_fn(features_text, edges_text): | |
| try: | |
| features = json.loads(features_text) | |
| edges = json.loads(edges_text) | |
| result = gnn_fraud.predict_nodes(features, edges) | |
| lines = [f"Node {p['node']}: {p['label']} (fraud prob: {p['fraud_probability']:.2%})" | |
| for p in result["predictions"]] | |
| return "\n".join(lines) | |
| except Exception as e: | |
| return f"❌ Error: {e}" | |
| # ---------- Module 04: Transformer ---------- | |
| def transformer_fn(text): | |
| result = transformer_reports.route(text) | |
| return result["department"], result["all_scores"] | |
| # ---------- Module 05: Autoencoder ---------- | |
| def leak_fn(readings_text): | |
| try: | |
| readings = [float(x.strip()) for x in readings_text.split(",") if x.strip()] | |
| result = autoencoder_leaks.detect(readings) | |
| emoji = {"normal": "🟢", "moderate": "🟡", "high": "🔴"}[result["severity"]] | |
| return ( | |
| f"{emoji} Severity: {result['severity'].upper()}\n" | |
| f"Anomaly score: {result['anomaly_score']:.6f}\n" | |
| f"Threshold: {result['threshold']:.6f}\n" | |
| f"Is leak: {result['is_leak']}" | |
| ) | |
| except Exception as e: | |
| return f"❌ Error: {e}" | |
| # ---------- Build UI ---------- | |
| with gr.Blocks(title="Smart City Monitor") as demo: | |
| gr.Markdown("# 🏙️ Smart City Monitor") | |
| gr.Markdown("Five neural network architectures in one demo.") | |
| with gr.Tab("🚦 Traffic Sign (CNN)"): | |
| with gr.Row(): | |
| img_in = gr.Image(type="filepath", label="Upload a traffic sign") | |
| lbl_out = gr.Label(num_top_classes=3, label="Prediction") | |
| img_in.change(cnn_fn, inputs=img_in, outputs=lbl_out) | |
| with gr.Tab("⚡ Energy Forecast (LSTM)"): | |
| hist_in = gr.Textbox( | |
| label="Last N daily kWh values (comma-separated)", | |
| value="1200,1150,1300,1250,1400,1350,1280,1220,1190,1310,1420,1380,1260,1240,1210,1330,1450,1400,1290,1270,1230,1180,1300,1440,1390,1270,1250,1220,1200,1340", | |
| lines=3, | |
| ) | |
| hist_out = gr.Textbox(label="Forecast") | |
| gr.Button("Predict").click(lstm_fn, inputs=hist_in, outputs=hist_out) | |
| with gr.Tab("🕸️ Fraud Detection (GNN)"): | |
| with gr.Row(): | |
| feat_in = gr.Code( | |
| label="Node features (JSON)", | |
| value='[[1.0,0.5],[0.5,1.0],[0.1,0.1],[5.0,5.0]]', | |
| language="json", | |
| ) | |
| edge_in = gr.Code( | |
| label="Edge index (JSON)", | |
| value='[[0,1,2,3],[1,2,3,0]]', | |
| language="json", | |
| ) | |
| gnn_out = gr.Textbox(label="Predictions", lines=6) | |
| gr.Button("Detect").click(gnn_fn, inputs=[feat_in, edge_in], outputs=gnn_out) | |
| with gr.Tab("📨 Citizen Reports (Transformer)"): | |
| text_in = gr.Textbox( | |
| label="Citizen report", | |
| value="There is a broken pipe leaking water on Elm Street.", | |
| lines=2, | |
| ) | |
| dept_out = gr.Textbox(label="Routed to") | |
| scores_out = gr.Label(label="All departments") | |
| gr.Button("Route").click(transformer_fn, inputs=text_in, outputs=[dept_out, scores_out]) | |
| with gr.Tab("💧 Leak Detection (Autoencoder)"): | |
| read_in = gr.Textbox( | |
| label="Sensor readings (comma-separated)", | |
| value="5.0,5.0,0.1,0.1,5.0,5.0,0.1,0.1,5.0,5.0", | |
| lines=2, | |
| ) | |
| leak_out = gr.Textbox(label="Result", lines=4) | |
| gr.Button("Detect").click(leak_fn, inputs=read_in, outputs=leak_out) | |
| if __name__ == "__main__": | |
| demo.launch(server_name="0.0.0.0", server_port=7860) | |