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import urllib.request
import fitz
import re
from openai import OpenAI
import gradio as gr
import os
import shutil
from pathlib import Path
import tensorflow_hub as hub
from tempfile import NamedTemporaryFile
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key=os.getenv('OPENROUTER_API_KEY')
)
from util import pdf_to_text, text_to_chunks, SemanticSearch
recommender = SemanticSearch()
def load_recommender(path, start_page=1):
global recommender
texts = pdf_to_text(path, start_page=start_page)
chunks = text_to_chunks(texts, start_page=start_page)
recommender.fit(chunks)
return 'Corpus Loaded.'
def generate_text(prompt):
message = client.chat.completions.create(
model="google/gemini-pro",
messages=[
{"role": "user", "content": prompt}
],
).choices[0].message.content
return message
def question_answer(chat_history, file, question):
suffix = Path(file.name).suffix
with NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
shutil.copyfile(file.name, tmp.name)
tmp_path = Path(tmp.name)
load_recommender(str(tmp_path))
answer = generate_text(question)
chat_history.append([question, answer])
return chat_history
title = 'PDF GPT '
description = """ PDF GPT """
with gr.Blocks(css="""#chatbot { font-size: 14px; min-height: 1200; }""") as demo:
gr.Markdown(f'<center><h3>{title}</h3></center>')
gr.Markdown(description)
with gr.Row():
with gr.Group():
with gr.Accordion("URL or pdf file"):
file = gr.File(label='Upload your PDF/ Research Paper / Book here', file_types=['.pdf'])
question = gr.Textbox(label='Enter your question here')
btn = gr.Button(value='Submit')
with gr.Group():
chatbot = gr.Chatbot(label="Chat History", elem_id="chatbot")
btn.click(
question_answer,
inputs=[chatbot, file, question],
outputs=[chatbot],
api_name="predict",
)
demo.launch(server_name="0.0.0.0") |