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import os
import gradio as gr
from langchain_core.prompts import PromptTemplate
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 torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from PIL import Image
import io
from threading import Thread
from transformers import TextIteratorStreamer
# Configure Gemini API
genai.configure(api_key=os.getenv("GOOGLE_API_KEY"))
# Load OpenELM model
checkpoint = "apple/OpenELM-270M"
checkpoint_tok = "meta-llama/Llama-2-7b-hf"
tokenizer = AutoTokenizer.from_pretrained(checkpoint_tok)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
torch_dtype = torch.bfloat16 if torch.cuda.is_available() else torch.float32
low_cpu_mem_usage = True if torch.cuda.is_available() else False
model = AutoModelForCausalLM.from_pretrained(checkpoint, torch_dtype=torch_dtype, trust_remote_code=True, low_cpu_mem_usage=low_cpu_mem_usage)
model.to(device)
# Adjust tokenizer settings
if tokenizer.pad_token is None:
tokenizer.pad_token = tokenizer.eos_token
tokenizer.pad_token_id = tokenizer.eos_token_id
# Define other settings
max_new_tokens = 250
repetition_penalty = 1.4
rtl = False
# Function to process PDF using Gemini API
def process_pdf(file_path, question):
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"])
pdf_loader = PyPDFLoader(file_path)
pages = pdf_loader.load_and_split()
context = "\n".join(str(page.page_content) for page in pages[:200])
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']
# Function to process images using Gemini API
def process_image(image, question):
model = genai.GenerativeModel('gemini-pro-vision')
response = model.generate_content([image, question])
return response.text
# Function to generate follow-up using OpenELM model
def generate_openelm_followup(answer):
prompt = f"Based on this answer: {answer}\nGenerate a follow-up question:"
inputs = tokenizer([prompt], return_tensors='pt').input_ids.to(model.device)
# Streaming output using TextIteratorStreamer
decode_kwargs = dict(skip_special_tokens=True, clean_up_tokenization_spaces=True)
streamer = TextIteratorStreamer(tokenizer, timeout=5., decode_kwargs=decode_kwargs)
generation_kwargs = dict(input_ids=inputs, streamer=streamer, max_new_tokens=max_new_tokens, repetition_penalty=repetition_penalty)
thread = Thread(target=model.generate, kwargs=generation_kwargs)
thread.start()
followup = ""
for new_text in streamer:
if new_text:
followup += new_text.replace(tokenizer.pad_token, "").replace(tokenizer.bos_token, "")
return followup
# Function to process input and generate output
def process_input(file, image, question):
try:
if file is not None:
gemini_answer = process_pdf(file.name, question)
elif image is not None:
gemini_answer = process_image(image, question)
else:
return "Please upload a PDF file or an image."
openelm_followup = generate_openelm_followup(gemini_answer)
combined_output = f"Gemini Answer: {gemini_answer}\n\nOpenELM Follow-up: {openelm_followup}"
return combined_output
except Exception as e:
return f"An error occurred: {str(e)}"
# Define Gradio Interface
with gr.Blocks() as demo:
gr.Markdown("# Multi-modal RAG Knowledge Retrieval using Gemini API and OpenELM Model")
with gr.Row():
with gr.Column():
input_file = gr.File(label="Upload PDF File")
input_image = gr.Image(type="pil", label="Upload Image")
input_question = gr.Textbox(label="Ask about the document or image")
output_text = gr.Textbox(label="Answer - Combined Gemini and OpenELM")
submit_button = gr.Button("Submit")
submit_button.click(fn=process_input, inputs=[input_file, input_image, input_question], outputs=output_text)
demo.launch()
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