import gradio as gr from transformers import pipeline import spaces pipe = pipeline("text-generation", model="fava-uw/fava-model") @spaces.GPU() def chat(refrence: str, passage: str) -> str: """ Help to chat with fava Args: refrence: Pass the refrence text here passage: main passage here """ text = f"Read the following references:\n{refrence}\nPlease identify all the errors in the following text using the information in the references provided and suggest edits if necessary:\n[Text] {passage}\n[Edited] " outputs = pipe(text) print(outputs) output = outputs[0]['generated_text'].split("[Edited]")[1] output = output.replace("", " ") output = output.replace("", " ") output = output.replace("", "") output = output.replace("", "") output = output.replace("", "") output = output.replace("", "") output = output.replace("", "") output = output.replace("", "") output = output.replace("", "") output = output.replace("", "") output = output.replace("", "") output = output.replace("", "") output = output.replace("", "") output = output.replace("", "") output = output.replace("", "") output = output.replace("", "") output = output.replace("Edited:", "") return f'
{output}
'; with gr.Blocks() as demo: gr.Markdown("# ExMC") with gr.Row(): refrence = gr.Textbox(label="Refrence", placeholder="Enter your Refrence here") passage = gr.Textbox(label="Passage", placeholder="Enter your passage") submit_btn = gr.Button("Submit") submit_btn.click(fn=chat, inputs=[refrence, passage], outputs=gr.HTML("Output result")) demo.launch()