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| import gradio as gr | |
| from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer | |
| # Load the pre-trained model from Hugging Face | |
| model_name = "llmModeluser/therapy_trained_model" | |
| model = AutoModelForCausalLM.from_pretrained(model_name) | |
| # Load the tokenizer directly from the Hugging Face model path | |
| tokenizer = AutoTokenizer.from_pretrained("llmModeluser/therapy_trained_model") | |
| # Create the text generation pipeline | |
| nlp = pipeline("text-generation", model=model, tokenizer=tokenizer) | |
| def chat_response(user_input): | |
| # Generate a response using the pre-trained model | |
| response = nlp(user_input, max_length=100, do_sample=True, top_k=50, top_p=0.95, num_return_sequences=1)[0]["generated_text"] | |
| return response | |
| iface = gr.Interface(fn=chat_response, | |
| inputs=gr.Textbox(lines=1, placeholder="Enter your message here..."), | |
| outputs="text", | |
| title="Therapy Chatbot") | |
| iface.launch(server_port=7861) |