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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| # Initialize the InferenceClient with your model from Hugging Face | |
| client = InferenceClient(model="pro-grammer/MindfulAI") | |
| def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p): | |
| # Build a prompt string manually | |
| prompt = system_message + "\n" | |
| for user_msg, assistant_msg in history: | |
| prompt += f"Human: {user_msg}\nAssistant: {assistant_msg}\n" | |
| prompt += f"Human: {message}\nAssistant:" | |
| response = "" | |
| # Use text_generation instead of chat_completion | |
| for token in client.text_generation( | |
| prompt, | |
| max_new_tokens=max_tokens, | |
| stream=True, | |
| temperature=temperature, | |
| top_p=top_p, | |
| ): | |
| # Depending on the API response structure, extract the generated text | |
| token_text = token.get("generated_text", "") | |
| response += token_text | |
| yield response | |
| if "Human" in response: | |
| location = response.find("Human") | |
| response = response[0:location] | |
| if "Me" in response: | |
| location = response.find("Me") | |
| response = response[0:location] | |
| if "You" in response: | |
| location = response.find("You") | |
| response = response[0:location] | |
| # Print disclaimer at the end | |
| print("""IMPORTANT: I am an AI project created to demonstrate therapeutic conversation patterns and am not a licensed mental health professional. If you're struggling with any emotional, mental health, or personal challenges, please seek help from a qualified therapist. You can find licensed therapists at BetterHelp.com. | |
| Remember, there's no substitute for professional mental healthcare. This is just a demonstration project.""") | |
| demo = gr.ChatInterface( | |
| fn=respond, | |
| title="MindfulAI Chat", | |
| description="Chat with MindfulAI – your AI Therapist powered by your model.", | |
| 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)"), | |
| ], | |
| ) | |
| if __name__ == "__main__": | |
| demo.launch() | |