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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| # Initialize the Zephyr-7B client | |
| zephyr_client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| # Load your fine-tuned GPT-2 model from Hugging Face | |
| MODEL_NAME = "hackergeek98/therapist01" # Replace with your model name | |
| gpt2_tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) | |
| gpt2_model = AutoModelForCausalLM.from_pretrained(MODEL_NAME) | |
| # Initialize conversation history for GPT-2 | |
| conversation_history = "" | |
| # Function to generate responses using Zephyr-7B | |
| def respond_with_zephyr( | |
| 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 = "" | |
| for message in zephyr_client.chat_completion( | |
| messages, | |
| max_tokens=max_tokens, | |
| stream=True, | |
| temperature=temperature, | |
| top_p=top_p, | |
| ): | |
| token = message.choices[0].delta.content | |
| response += token | |
| yield response | |
| # Function to generate responses using GPT-2 | |
| def respond_with_gpt2(user_input): | |
| global conversation_history | |
| # Update conversation history with user input | |
| conversation_history += f"User: {user_input}\n" | |
| # Tokenize the conversation history | |
| inputs = gpt2_tokenizer(conversation_history, return_tensors="pt", truncation=True, max_length=1024) | |
| # Generate a response from the model | |
| outputs = gpt2_model.generate(inputs['input_ids'], max_length=1024, num_return_sequences=1, no_repeat_ngram_size=2) | |
| # Decode the model's output | |
| response = gpt2_tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| # Update conversation history with the model's response | |
| conversation_history += f"Therapist: {response}\n" | |
| # Return the therapist's response | |
| return response | |
| # Function to handle the model selection and response generation | |
| def respond(message, history, model_choice, system_message, max_tokens, temperature, top_p): | |
| if model_choice == "Zephyr-7B": | |
| return respond_with_zephyr(message, history, system_message, max_tokens, temperature, top_p) | |
| elif model_choice == "GPT-2 Therapist": | |
| return respond_with_gpt2(message) | |
| else: | |
| return "Invalid model selection." | |
| # Create Gradio interface | |
| demo = gr.ChatInterface( | |
| respond, | |
| additional_inputs=[ | |
| gr.Dropdown(choices=["Zephyr-7B", "GPT-2 Therapist"], label="Model", value="Zephyr-7B"), | |
| gr.Textbox(value="You are a friendly Chatbot.", 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="Multi-Model Chat Interface", | |
| description="Choose between Zephyr-7B and a fine-tuned GPT-2 model to chat with." | |
| ) | |
| # Launch the app | |
| if __name__ == "__main__": | |
| demo.launch() |