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Sleeping
| 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, | |
| ): | |
| # Initialize messages with system instructions | |
| messages = [{"role": "system", "content": system_message}] | |
| # Add historical conversation | |
| 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 new user message | |
| messages.append({"role": "user", "content": message}) | |
| response = "" | |
| # Get the model's response | |
| for response_chunk in client.chat_completion( | |
| messages, | |
| max_tokens=max_tokens, | |
| stream=True, | |
| temperature=temperature, | |
| top_p=top_p, | |
| ): | |
| token = response_chunk.choices[0].delta.content | |
| response += token | |
| response = response.strip() | |
| # Enhanced context-specific relevance check | |
| if is_relevant_to_constitution(response): | |
| return response | |
| # If response does not meet relevance criteria | |
| return "Sorry, I can only provide information related to the Constitution of India. Please ask a question related to the Constitution." | |
| def is_relevant_to_constitution(response): | |
| # Keywords to check in the response | |
| relevant_keywords = [ | |
| "constitution", "article", "law", "legal", "rights", "act", "judiciary", | |
| "legislature", "executive", "fundamental", "amendment", "provision", | |
| "policy", "directive", "supreme court", "high court", "legislation", | |
| "government", "election", "parliament", "state", "central", "reform", | |
| "citizen", "equality", "democracy", "directive principles", "fundamental duties", | |
| "preamble", "enforcement", "federalism", "separation of powers", "justice", | |
| "republic", "state legislature", "union territory", "bill", "ordinance", | |
| "convention", "charter", "treaty", "declaration", "proclamation", "amendments", | |
| "compensation", "grievance", "judicial review", "secularism", "socialism", | |
| "pluralism", "sovereignty", "autonomy", "independence", "integrity", "caste", | |
| "reservation", "minorities", "discrimination", "fundamental rights", | |
| "emergency", "state emergency", "national emergency", "local bodies", | |
| "tribunal", "ombudsman", "civil rights", "criminal justice", "human rights" | |
| ] | |
| # Check if response contains relevant keywords | |
| return any(keyword in response.lower() for keyword in relevant_keywords) | |
| # Create the Gradio chat interface | |
| demo = gr.ChatInterface( | |
| respond, | |
| additional_inputs=[ | |
| gr.Textbox(value="You are a knowledgeable assistant specializing in the Constitution of India. Answer only questions related to the Constitution. If the answer does not contain relevant constitutional keywords, inform the user accordingly.", label="System message", visible=False), | |
| 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() | |