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import os
from langchain_core.prompts import PromptTemplate
from langchain.chains.question_answering import load_qa_chain
from langchain_community.document_loaders import PyPDFLoader
import google.generativeai as genai
import gradio as gr
# Function for initialization
def initialize(pdf_file, question):
try:
# Save the uploaded PDF content temporarily
with open("/tmp/uploaded_file.pdf", "wb") as f:
f.write(pdf_file.read())
file_path = "/tmp/uploaded_file.pdf"
# Configure Google Generative AI
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
model = genai.GenerativeModel('gemini-pro')
model = ChatGoogleGenerativeAI(model="gemini-pro", temperature=0.3)
# Prompt template for formatting context and question
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"
Context:
{context}
Question:
{question}
Answer:
"""
prompt = PromptTemplate(template=prompt_template, input_variables=["context", "question"])
# Process the PDF if it exists
if os.path.exists(file_path):
pdf_loader = PyPDFLoader(file_path)
pages = pdf_loader.load_and_split()
context = "\n".join(str(page.page_content) for page in pages[:30]) # Limit to first 30 pages
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']
else:
return "Error: Unable to process the document. Please ensure the PDF file is valid."
except Exception as e:
return f"An error occurred: {e}" # Generic error handling
# Create a Gradio interface
interface = gr.Interface(
fn=initialize,
inputs=[
gr.File(label="Upload PDF"), # No need for 'type' argument
gr.Textbox(label="Question")
],
outputs="text",
title="GeminiPro Q&A Bot",
description="Ask questions about the uploaded PDF document.",
)
# Launch the interface
interface.launch()
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