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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| """ | |
| For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference | |
| """ | |
| client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| 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 = "" | |
| 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 | |
| yield response | |
| """ | |
| For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface | |
| """ | |
| demo = gr.ChatInterface( | |
| respond, | |
| additional_inputs=[ | |
| 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)", | |
| ), | |
| ], | |
| ) | |
| import gradio as gr | |
| from transformers import AutoModelForCausalLM, AutoTokenizer | |
| # Load your fine-tuned GPT-2 model from Hugging Face | |
| MODEL_NAME = "hackergeek98/therapist01" # Replace w | |
| tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME) | |
| model = AutoModelForCausalLM.from_pretrained(MODEL_NAME) | |
| # Initialize conversation history | |
| conversation_history = "" | |
| # Function to generate responses | |
| def generate_response(user_input): | |
| global conversation_history | |
| # Update conversation history with user input | |
| conversation_history += f"User: {user_input}\n" | |
| # Tokenize the conversation history | |
| inputs = tokenizer(conversation_history, return_tensors="pt", truncation=True, max_length=1024) | |
| # Generate a response from the model | |
| outputs = model.generate(inputs['input_ids'], max_length=1024, num_return_sequences=1, no_repeat_ngram_size=2) | |
| # Decode the model's output | |
| response = 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 | |
| # Create Gradio interface | |
| interface = gr.Interface(fn=generate_response, | |
| inputs=gr.Textbox(label="Enter your message", lines=2), | |
| outputs=gr.Textbox(label="Therapist Response", lines=2), | |
| title="Virtual Therapist", | |
| description="A fine-tuned GPT-2 model acting as a virtual therapist. Chat with the model and receive responses as if you are talking to a therapist.") | |
| # Launch the app | |
| interface.launch() | |
| if __name__ == "__main__": | |
| demo.launch() | |