Delete app.py

app.py

@@ -1,55 +0,0 @@
-import streamlit as st
-import torch
-
-from transformers import Blip2Processor, Blip2ForConditionalGeneration
-
-
-def load_caption_model():
-    # Quantization Config
-    bnb_config = BitsAndBytesConfig(
-    load_in_8bit=True,
-    bnb_8bit_quant_type="nf4",
-    bnb_8bit_compute_dtype=torch.float16,
-    bnb_8bit_use_double_quant=False
-    )
-
-
-
-
-    processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
-    model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", load_in_8bit=True,torch_dtype=torch.float16, device_map="auto")
-
-    return, model, processor
-
-    
-
-def answer_question(image, question, model, processor):
-    
-    
-    image = Image.open(image).convert('RGB')
-
-    inputs = processor(image, question, return_tensors="pt").to("cuda", torch.float16)
-
-    out = model.generate(**inputs, max_length=200, min_length=20, num_beams=1)
-
-    answer = processor.decode(out[0], skip_special_tokens=True).strip()
-    return answer
-
-st.title("Image Question Answering")
-
-# File uploader for the image
-image = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])
-
-# Text input for the question
-question = st.text_input("Enter your question about the image:")
-
-if st.button("Get Answer"):
-    if image is not None and question:
-        # Display the image
-        st.image(image, use_column_width=True)
-        # Get and display the answer
-        model, processor = load_caption_model()
-        answer = answer_question(image, question, model, processor)
-        st.write(answer)
-    else:
-        st.write("Please upload an image and enter a question.")