fixed

app.py

@@ -0,0 +1,45 @@
+import streamlit as st
+import torch
+
+from transformers import Blip2Processor, Blip2ForConditionalGeneration
+
+
+def load_model():
+
+    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.")