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()