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import streamlit as st
from PIL import Image
from transformers import BlipProcessor, Blip2ForConditionalGeneration,BlipForQuestionAnswering
import torch
@st.cache_resource
def load_blip_model():
device = "cuda" if torch.cuda.is_available() else "cpu"
processor = BlipProcessor.from_pretrained("Salesforce/blip-vqa-base")
model = BlipForQuestionAnswering.from_pretrained(
"Salesforce/blip-vqa-base",
torch_dtype=torch.float16 if device == "cuda" else torch.float32
).to(device)
return processor, model, device
def answer_question(image, question, processor, model, device):
inputs = processor(image, question, return_tensors="pt").to(device)
outputs = model.generate(**inputs, max_new_tokens=100)
return processor.decode(outputs[0], skip_special_tokens=True)
# Streamlit App
def main():
st.title("Image Chat Assistant")
# Load model
processor, model, device = load_blip_model()
# Image upload
uploaded_file = st.file_uploader("Upload image", type=["jpg", "png", "jpeg"])
if uploaded_file:
image = Image.open(uploaded_file)
st.image(image, use_column_width=True)
col1, col2, col3 = st.columns([0.33,0.33,0.33])
with col1:
detect= st.button("🔍 Detect Objects", key="btn1")
with col2:
describe= st.button("📝 Describe Image", key="btn2")
with col3:
story= st.button("📖 Generate Story", key="btn3")
# Chat interface
if "messages" not in st.session_state:
st.session_state.messages = []
chat_container = st.container(height=400)
with chat_container:
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
if prompt := st.chat_input("Ask about the image"):
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.markdown(prompt)
with st.chat_message("assistant"):
with st.spinner("Thinking..."):
response = answer_question(image, prompt, processor, model, device)
#response= "response sample"
st.markdown(response)
st.session_state.messages.append({"role": "assistant", "content": response})
if __name__ == "__main__":
main() |