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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| def respond( | |
| message, | |
| history: list[tuple[str, str]], | |
| system_message, | |
| max_tokens, | |
| temperature, | |
| top_p, | |
| ): | |
| # Construct the messages with prompt engineering | |
| messages = [{"role": "system", "content": system_message}] | |
| for val in history: | |
| if val[0]: | |
| messages.append({"role": "user", "content": val[0]}) | |
| if val[1]: | |
| messages.append({"role": "assistant", "content": val[1]}) | |
| # Add the user message | |
| messages.append({"role": "user", "content": message}) | |
| # Generate the response | |
| response = "" | |
| for message in client.chat_completion( | |
| messages, | |
| max_tokens=max_tokens, | |
| stream=True, | |
| temperature=temperature, | |
| top_p=top_p, | |
| ): | |
| token = message.choices[0].delta.content | |
| response += token | |
| # Check if the response is empty or if required fields are missing | |
| if not response.strip(): | |
| yield "No relevant information could be generated based on the provided query." | |
| yield response | |
| # Enhanced system message for structured output | |
| default_system_message = ( | |
| "You are part of a geospatial AI agent/assistant and are aiding in parsing the query. When a user asks something," | |
| "You are a specialized parser for a geospatial AI assistant that analyzes city open data. " | |
| "Your task is to extract structured information from user queries about city data. " | |
| "Respond with the following format ONLY (no other text):\n\n" | |
| "city: [City Name or blank if unclear]\n" | |
| "state: [State Name or blank if unclear]\n" | |
| "dataset1: [Primary Dataset Type]\n" | |
| "dataset2: [Secondary Dataset Type if applicable, otherwise leave blank]\n" | |
| "time_filter: [any time filters mentioned - today, yesterday, last_week, last_month, this_year, etc.]\n" | |
| #"spatial_filter: [any spatial filters from - proximity, Boundary, near, within or distance]\n" | |
| "count_filter: [any count or limit filters]\n" | |
| "operations: [any sequencial GIS operations needed from - append_gdfs, calculate_aspect, buffer_features, calculate_field, calculate_geometry_attributes, clip_features, coordinate_system_transformation, dissolve_features, excel_to_table, export_to_geojson, feature_to_point, generate_near_table, geocode_address, reverse_geocode, create_heatmap, intersect_features, join_field, map_layout, merge_datasets, near_analysis, network_analysis_suite, project_features, service_area, shortest_path, slope_calculation, spatial_data_validation, spatial_join, spatial_statistics_hotspot, split_features, symbolization, table_to_excel, union_features]\n\n" | |
| "Provide only the extracted information without explanation. If information is not present in the query, leave that field blank." | |
| ) | |
| demo = gr.ChatInterface( | |
| respond, | |
| additional_inputs=[ | |
| gr.Textbox(value=default_system_message, label="System message"), | |
| gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
| gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
| gr.Slider( | |
| minimum=0.1, | |
| maximum=1.0, | |
| value=0.95, | |
| step=0.05, | |
| label="Top-p (nucleus sampling)", | |
| ), | |
| ], | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() |