import streamlit as st from io import BytesIO from PIL import Image from transformers import ViltProcessor, ViltForQuestionAnswering import requests import torch import torchvision from langchain_google_genai import GoogleGenerativeAI from langchain_google_genai import ChatGoogleGenerativeAI from langchain.prompts import PromptTemplate from langchain.chains import LLMChain from langchain.chat_models import ChatOpenAI from transformers import AutoProcessor, AutoModelForCausalLM from huggingface_hub import hf_hub_download from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor from transformers import BlipProcessor, BlipForConditionalGeneration import os # os.environ["OPENAI_API_KEY"] = os.getenv('OPENAI_API_KEY') os.environ["GOOGLE_API_KEY"] = os.getenv('GOOGLE_API_KEY') # llm = ChatOpenAI(temperature=0.2, model_name="gpt-3.5-turbo") llm = ChatGoogleGenerativeAI(temperature=0.2, model="gemini-pro") prompt = PromptTemplate( input_variables=["question", "elements"], template="""You are a helpful assistant that can answer question related to an image. You have the ability to see the image and answer questions about it. I will give you a question and element about the image and you will answer the question. \n\n #Question: {question} #Elements: {elements} \n\n Your structured response:""", ) def convert_png_to_jpg(image): rgb_image = image.convert('RGB') byte_arr = BytesIO() rgb_image.save(byte_arr, format='JPEG') byte_arr.seek(0) return Image.open(byte_arr) def vilt(image, query): processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa") model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa") encoding = processor(image, query, return_tensors="pt") outputs = model(**encoding) logits = outputs.logits idx = logits.argmax(-1).item() sol = model.config.id2label[idx] return sol def blip(image, query): processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large") model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large") # unconditional image captioning inputs = processor(image, return_tensors="pt") out = model.generate(**inputs) sol = processor.decode(out[0], skip_special_tokens=True) return sol def GIT(image, query): processor = AutoProcessor.from_pretrained("microsoft/git-base-textvqa") model = AutoModelForCausalLM.from_pretrained("microsoft/git-base-textvqa") # file_path = hf_hub_download(repo_id="nielsr/textvqa-sample", filename="bus.png", repo_type="dataset") # image = Image.open(file_path).convert("RGB") pixel_values = processor(images=image, return_tensors="pt").pixel_values question = query input_ids = processor(text=question, add_special_tokens=False).input_ids input_ids = [processor.tokenizer.cls_token_id] + input_ids input_ids = torch.tensor(input_ids).unsqueeze(0) generated_ids = model.generate(pixel_values=pixel_values, input_ids=input_ids, max_length=50) response = processor.batch_decode(generated_ids, skip_special_tokens=True) generated_ids_1 = model.generate(pixel_values=pixel_values, max_length=50) generated_caption = processor.batch_decode(generated_ids_1, skip_special_tokens=True)[0] return response[0] + " " + generated_caption @st.cache_data(show_spinner="Processing image...") def generate_table(uploaded_file): image = Image.open(uploaded_file) print("graph start") model = Pix2StructForConditionalGeneration.from_pretrained('google/deplot') processor = Pix2StructProcessor.from_pretrained('google/deplot') print("graph start 1") inputs = processor(images=image, text="Generate underlying data table of the figure below and give the text as well:", return_tensors="pt") predictions = model.generate(**inputs, max_new_tokens=512) print("end") table = processor.decode(predictions[0], skip_special_tokens=True) print(table) return table def process_query(image, query): blip_sol = blip(image, query) vilt_sol = vilt(image, query) GIT_sol = GIT(image, query) llm_sol = blip_sol + " " + vilt_sol + " " + GIT_sol print(llm_sol) chain = LLMChain(llm=llm, prompt=prompt) response = chain.run(question=query, elements=llm_sol) return response def process_query_graph(data_table, query): prompt = PromptTemplate( input_variables=["question", "elements"], template="""You are a helpful assistant capable of answering questions related to graph images. You possess the ability to view the graph image and respond to inquiries about it. I will provide you with a question and the associated data table of the graph, and you will answer the question \n\n #Question: {question} #Elements: {elements} \n\n Your structured response:""", ) chain = LLMChain(llm=llm, prompt=prompt) response = chain.run(question=query, elements=data_table) return response def chart_with_Image(): st.header("Chat with Image", divider='rainbow') uploaded_file = st.file_uploader('Upload your IMAGE', type=['png', 'jpeg', 'jpg'], key="imageUploader") if uploaded_file is not None: image = Image.open(uploaded_file) # ViLT model only supports JPG images if image.format == 'PNG': image = convert_png_to_jpg(image) st.image(image, caption='Uploaded Image.', width=300) cancel_button = st.button('Cancel') query = st.text_input('Ask a question to the IMAGE') if query: with st.spinner('Processing...'): answer = process_query(image, query) st.write(answer) if cancel_button: st.stop() def chat_with_graph(): st.header("Chat with Graph", divider='rainbow') uploaded_file = st.file_uploader('Upload your GRAPH', type=['png', 'jpeg', 'jpg'], key="graphUploader") if uploaded_file is not None: image = Image.open(uploaded_file) if image.format == 'PNG': image = convert_png_to_jpg(image) # data_table = generate_table(uploaded_file) st.image(image, caption='Uploaded Image.') data_table = generate_table(uploaded_file) cancel_button = st.button('Cancel') query = st.text_input('Ask a question to the IMAGE') if query: with st.spinner('Processing...'): answer = process_query_graph(data_table, query) st.write(answer) if cancel_button: st.stop() st.title("VisionQuery") option = st.selectbox( "Who would you like to chart with?", ("Image", "Graph"), index=None, placeholder="Select contact method...", ) st.write('You selected:', option) if option == "Image": chart_with_Image() elif option == "Graph": chat_with_graph()