| import gradio as gr | |
| import random | |
| import time | |
| from transformers import pipeline,AutoModelForSeq2SeqLM,AutoTokenizer | |
| model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base") | |
| tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-base") | |
| context="" | |
| def generate_answer(question): | |
| prompt = question +". \nAnswer this question given context in next line if answer is present in context otherwise say I don't know about that. Context: \n "+context | |
| inputs = tokenizer(prompt , return_tensors="pt") | |
| outputs = model.generate(**inputs) | |
| return (tokenizer.batch_decode(outputs, skip_special_tokens=True)) | |
| def upload_file(file): | |
| global context | |
| with open(file.name, encoding="utf-8") as f: | |
| context = f.read() | |
| with gr.Blocks() as demo: | |
| file_output = gr.File() | |
| upload_button = gr.UploadButton("Click to Upload a File", file_types=["txt", "pdf"]) | |
| upload_button.upload(upload_file, upload_button, file_output) | |
| chatbot = gr.Chatbot() | |
| msg = gr.Textbox() | |
| clear = gr.ClearButton([msg, chatbot,upload_button]) | |
| def respond(message, chat_history): | |
| ans=generate_answer(message) | |
| chat_history.append((message, f"\n {ans} ")) | |
| return "", chat_history | |
| msg.submit(respond, [msg, chatbot], [msg, chatbot]) | |
| with gr.Row(visible=True) as button_row: | |
| upvote_btn = gr.Button(value="👍 Upvote", interactive=True) | |
| downvote_btn = gr.Button(value="👎 Downvote", interactive=True) | |
| demo.queue() | |
| demo.launch(debug=True) |