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import os
import gradio as gr
import asyncio
from langchain_core.prompts import PromptTemplate
from langchain_community.output_parsers.rail_parser import GuardrailsOutputParser
from langchain_community.document_loaders import PyPDFLoader
from langchain_google_genai import ChatGoogleGenerativeAI
import google.generativeai as genai
from langchain.chains.question_answering import load_qa_chain # Import load_qa_chain
async def initialize(file_path, question):
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"])
if os.path.exists(file_path):
pdf_loader = PyPDFLoader(file_path)
pages = pdf_loader.load_and_split()
context = "\n".join(f"Page {i+1}: {page.page_content}" for i, page in enumerate(pages[:30]))
stuff_chain = load_qa_chain(model, chain_type="stuff", prompt=prompt)
# Use ainvoke instead of arun
stuff_answer = await stuff_chain.ainvoke({"input_documents": pages, "question": question, "context": context})
# Extract the page number where the context was found
sources = []
for i, page in enumerate(pages):
if question.lower() in page.page_content.lower():
sources.append(f"Page {i+1}")
if sources:
source_str = f" (Source: {', '.join(sources)})"
else:
source_str = " (Source: Not found in specific page)"
# Add the clickable link to the source
file_name = os.path.basename(file_path)
source_link = f"[{file_name}](file://{os.path.abspath(file_path)})"
return f"{stuff_answer} {source_str} - [Document: {source_link}]"
else:
return "Error: Unable to process the document. Please ensure the PDF file is valid."
# Define Gradio Interface
input_file = gr.File(label="Upload PDF File")
input_question = gr.Textbox(label="Ask about the document")
output_text = gr.Textbox(label="Answer - GeminiPro")
async def pdf_qa(file, question):
answer = await initialize(file.name, question)
return answer
# Create Gradio Interface with share=True to enable a public link
gr.Interface(fn=pdf_qa, inputs=[input_file, input_question], outputs=output_text, title="PDF Question Answering System", description="Upload a PDF file and ask questions about the content.").launch(share=True)
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