app.py

#!pip install streamlit


# Install necessary libraries
!pip install PyPDF2 transformers torch accelerate streamlit

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import PyPDF2
import streamlit as st

# Step 1: Extract text from PDF
def extract_text_from_pdf(pdf_path):
    pdf_text = ""
    with open(pdf_path, "rb") as file:
        reader = PyPDF2.PdfReader(file)
        for page_num in range(len(reader.pages)):
            page = reader.pages[page_num]
            pdf_text += page.extract_text()
    return pdf_text

# Step 2: Initialize the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained("ricepaper/vi-gemma-2b-RAG")
model = AutoModelForCausalLM.from_pretrained(
    "ricepaper/vi-gemma-2b-RAG",
    device_map="auto",
    torch_dtype=torch.bfloat16
)

# Use GPU if available
if torch.cuda.is_available():
    model.to("cuda")

# Step 3: Define the prompt format for the model
prompt = """
### Instruction and Input:
Based on the following context/document:
{}
Please answer the question: {}

### Response:
{}
"""

# Step 4: Function to generate answer based on query and context
def generate_answer(context, query):
    input_text = prompt.format(context, query, "")
    input_ids = tokenizer(input_text, return_tensors="pt")

    # Use GPU for input ids if available
    if torch.cuda.is_available():
        input_ids = input_ids.to("cuda")

    # Generate text using the model
    outputs = model.generate(
        **input_ids,
        max_new_tokens=500,
        no_repeat_ngram_size=5,
    )

    # Decode and print the results
    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
    return answer

# Step 5: Read PDF and generate answer
pdf_path = '/content/monopoly.pdf'  # Replace with your PDF file path
pdf_text = extract_text_from_pdf(pdf_path)
query = "Your question here"  # Replace with your question

# Generate answer based on extracted PDF text and the query
answer = generate_answer(pdf_text, query)
print("Answer:", answer)