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import urllib.request |
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import fitz |
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import re |
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import numpy as np |
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import tensorflow_hub as hub |
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from openai import OpenAI |
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import gradio as gr |
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import os |
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import shutil |
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from pathlib import Path |
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from tempfile import NamedTemporaryFile |
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from sklearn.neighbors import NearestNeighbors |
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import huggingface_hub |
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clinet = OpenAI( |
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base_url='https://api-inference.huggingface.co/models/mistralai/Mixtral-8x7B-Instruct-v0.1/v1/', |
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api_key=os.getenv('key') |
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) |
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from util import pdf_to_text, text_to_chunks, SemanticSearch |
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recommender = SemanticSearch() |
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def load_recommender(path, start_page=1): |
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global recommender |
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texts = pdf_to_text(path, start_page=start_page) |
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chunks = text_to_chunks(texts, start_page=start_page) |
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recommender.fit(chunks) |
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return 'Corpus Loaded.' |
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def generate_text(prompt, model = "gpt-3.5-turbo-16k-0613"): |
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model="mistralai/Mixtral-8x7B-Instruct-v0.1" |
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max_tokens=256 |
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message = clinet.chat.completions.create( |
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model=model, |
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messages=[ |
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{"role": "system", "content": "You are a helpful assistant."}, |
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{"role": "assistant", "content": "Here is some initial assistant message."}, |
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{"role": "user", "content": prompt} |
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], |
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max_tokens=max_tokens, |
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).choices[0].message['content'] |
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return message |
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def generate_answer(question): |
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topn_chunks = recommender(question) |
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prompt = 'search results:\n\n' |
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for c in topn_chunks: |
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prompt += c + '\n\n' |
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prompt += "Instructions: Compose a comprehensive reply to the query using the search results given. "\ |
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"Cite each reference using [ Page Number] notation. "\ |
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"Only answer what is asked. The answer should be short and concise. \n\nQuery: " |
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prompt += f"{question}\nAnswer:" |
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answer = generate_text(prompt) |
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return answer |
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def question_anwser(chat_history, file, question): |
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suffix = Path(file.name).suffix |
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with NamedTemporaryFile(delete=False, suffix=suffix) as tmp: |
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shutil.copyfile(file.name, tmp.name) |
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tmp_path = Path(tmp.name) |
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load_recommender(str(tmp_path)) |
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answer = generate_answer(question) |
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chat_history.append([question, answer]) |
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return chat_history |
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title = 'PDF GPT ' |
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description = """ PDF GPT """ |
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with gr.Blocks(css="""#chatbot { font-size: 14px; min-height: 1200; }""") as demo: |
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gr.Markdown(f'<center><h3>{title}</h3></center>') |
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gr.Markdown(description) |
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with gr.Row(): |
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with gr.Group(): |
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with gr.Accordion("URL or pdf file"): |
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file = gr.File(label='Upload your PDF/ Research Paper / Book here', file_types=['.pdf']) |
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question = gr.Textbox(label='Enter your question here') |
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btn = gr.Button(value='Submit') |
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with gr.Group(): |
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chatbot = gr.Chatbot(label="Chat History", elem_id="chatbot") |
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btn.click( |
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question_anwser, |
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inputs=[chatbot, file, question], |
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outputs=[chatbot], |
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api_name="predict", |
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) |
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demo.launch(server_name="0.0.0.0") |
