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import urllib
import warnings
from pathlib import Path
import os
import gradio as gr
from langchain import PromptTemplate
from langchain.chains.question_answering import load_qa_chain
from langchain.document_loaders import PyPDFLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_google_genai import ChatGoogleGenerativeAI
import google.generativeai as genai
import pandas as pd
from dotenv import load_dotenv
load_dotenv() # take environment variables from .env.
# Fungsi untuk inisialisasi
def initialize(link, question):
# Konfigurasikan kunci API
os.getenv("GOOGLE_API_KEY")
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
model = genai.GenerativeModel('gemini-pro')
model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
prompt_template = """Answer the question as precise as possible using the provided context. If the answer is
not contained in the context, say "answer not available in context" \n\n
Context: \n {context}?\n
Question: \n {question} \n
Answer:
"""
prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
# Download PDF dari URL yang diberikan
pdf_file = "downloaded_paper.pdf"
urllib.request.urlretrieve(link, pdf_file)
# Load the PDF
pdf_loader = PyPDFLoader(pdf_file)
pages = pdf_loader.load_and_split()
# Process the file content and use it as the context
context = "\n".join(str(page.page_content) for page in pages[:30])
stuff_chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
stuff_answer = stuff_chain({"input_documents": pages, "question": question, "context": context}, return_only_outputs=True)
return stuff_answer['output_text']
# Membuat antarmuka pengguna
with gr.Blocks() as demo:
gr.Markdown('# RAG Q&A Bot with Gemini - Pro')
gr.Markdown('### Hands-On LLM')
link_input = gr.Textbox(label="Input Link Paper atau PDF", placeholder="Paste PDF disini")
question_input = gr.Textbox(label="Tanyakan Dokumen", placeholder="Tanyakan Dokumen:")
chatbot = gr.Textbox(label="Answer - GeminiPro")
ask_button = gr.Button("Ask Question")
ask_button.click(initialize, inputs=[link_input, question_input], outputs=[chatbot])
# Meluncurkan antarmuka pengguna
demo.queue().launch(debug=True)
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