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| import gradio as gr | |
| import os | |
| from huggingface_hub import InferenceClient | |
| from huggingface_hub.inference._generated.types.chat_completion import ChatCompletionStreamOutput | |
| # Use the fine-tuned maritime legal model | |
| MODEL = "nomiChroma3.1" | |
| client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| from huggingface_hub.inference._generated.types.chat_completion import ChatCompletionStreamOutput | |
| def respond( | |
| message, | |
| history: list[tuple[str, str]], | |
| system_message, | |
| max_tokens, | |
| temperature, | |
| top_p, | |
| ): | |
| 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]}) | |
| messages.append({"role": "user", "content": message}) | |
| response = "" | |
| try: | |
| for message in client.chat_completion( | |
| messages, | |
| max_tokens=max_tokens, | |
| stream=True, | |
| temperature=temperature, | |
| top_p=top_p, | |
| ): | |
| try: | |
| if isinstance(message, ChatCompletionStreamOutput): | |
| content = message.choices[0].delta.content | |
| if content is not None: | |
| response += content | |
| yield response | |
| if message.choices[0].finish_reason == 'stop': | |
| break | |
| elif isinstance(message, dict): | |
| content = message.get('choices', [{}])[0].get('delta', {}).get('content') | |
| if content: | |
| response += content | |
| yield response | |
| if message.get('choices', [{}])[0].get('finish_reason') == 'stop': | |
| break | |
| elif isinstance(message, str): | |
| if message.strip(): # Only process non-empty strings | |
| response += message | |
| yield response | |
| else: | |
| print(f"Unexpected message type: {type(message)}") | |
| print(f"Message content: {message}") | |
| except Exception as e: | |
| print(f"Error processing message: {e}") | |
| print(f"Problematic message: {message}") | |
| continue # Continue to the next message even if there's an error | |
| # Final yield to ensure all content is returned | |
| if response: | |
| yield response | |
| except Exception as e: | |
| print(f"An error occurred in the main loop: {e}") | |
| if response: | |
| yield response | |
| else: | |
| yield f"An error occurred: {e}" | |
| # Gradio interface setup | |
| demo = gr.ChatInterface( | |
| respond, | |
| additional_inputs=[ | |
| gr.Textbox( | |
| value="You are a maritime legal assistant with expertise strictly in Indian maritime law. Provide detailed legal advice and information based on Indian maritime legal principles and regulations.", | |
| 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)", | |
| ), | |
| ], | |
| title="Maritime Legal Compliance", | |
| description="This chatbot uses Fine-tuned LLAMA-3.1 model personalised specifically to provide assistance with Indian maritime legal queries.", | |
| theme="soft", | |
| examples=[ | |
| ["What are the key regulations governing ports in India?"], | |
| ["Explain the concept of cabotage in Indian maritime law."], | |
| ["What are the legal requirements for registering a vessel in India?"], | |
| ], | |
| cache_examples=False, | |
| ) | |
| # Launch the Gradio app | |
| if __name__ == "_main_": | |
| demo.launch() |