app.py

import os
from langchain import PromptTemplate
from langchain.chains.question_answering import load_qa_chain
from langchain.document_loaders import PyPDFLoader
from langchain_google_genai import ChatGoogleGenerativeAI
import google.generativeai as genai
import gradio as gr

# Function for initialization
def initialize(pdf_file, question):
    with open("/tmp/uploaded_file.pdf", "wb") as f:
        f.write(pdf_file.read())
    file_path = "/tmp/uploaded_file.pdf"
    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(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']
    else:
        return "Error: Unable to process the document. Please ensure the PDF file is valid."

# Create a Gradio interface for file upload and question input
iface = gr.Interface(
    fn=initialize,
    inputs=[
        gr.File(label="Upload PDF", type="file"),
        gr.Textbox(label="Question")
    ],
    outputs="text",
    title="GeminiPro Q&A Bot",
    description="Ask questions about the uploaded PDF document.",
)

# Launch the interface
iface.launch()