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import gradio as gr | |
from transformers import AutoModelForCausalLM, AutoTokenizer | |
model_name = "UrFavB0i/Fine-tuned-Falcon7B-skincare-chatbot" | |
model = AutoModelForCausalLM.from_pretrained(model_name) | |
tokenizer = AutoTokenizer.from_pretrained(model_name) | |
def predict(history, input_text): | |
history.append({"role": "user", "content": input_text}) | |
inputs = tokenizer(" ".join([item["content"] for item in history if item["role"] == "user"]), return_tensors="pt") | |
outputs = model.generate(**inputs) | |
response = tokenizer.decode(outputs[0], skip_special_tokens=True) | |
history.append({"role": "bot", "content": response}) | |
return history, history | |
iface = gr.Interface( | |
fn=predict, | |
inputs=[gr.inputs.State(), gr.inputs.Textbox(lines=2, placeholder="Enter text here...")], | |
outputs=["state", "chatbot"] | |
) | |
iface.launch() | |