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import gradio as gr
import os
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
"""
model_name = "meta-llama/Llama-3.2-1B"
huggingface_token = os.getenv("SECRET_ENV_VARIABLE")
#client = InferenceClient(api_key=huggingface_token)
client = InferenceClient(model=model_name, token=huggingface_token)
def generate_text(
prompt,
system_message,
max_tokens,
temperature,
top_p
):
try:
print(f"Attempting to generate text for prompt: {prompt[:50]}...")
response = client.text_generation(
prompt,
max_new_tokens=max_tokens,
temperature=temperature,
top_k=50,
top_p=top_p,
do_sample=True
)
print(f"Generated text: {response[:100]}...")
return response
except Exception as e:
print(f"Error in generate_text: {type(e).__name__}: {str(e)}")
return f"An error occurred: {type(e).__name__}: {str(e)}"
with gr.Blocks() as demo:
gr.Markdown("Q&A App")
with gr.Tab("Q&A"):
Query = gr.Textbox(label="Query")
generate_button = gr.Button("Ask Query")
output = gr.Textbox(label="Generated Answer", lines=10)
generate_button.click(generate_text,
#inputs=[industry, recipient_role, company_details],
inputs=[
Query,
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)",
),
],
outputs=output)
if __name__ == "__main__":
demo.launch()
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