Create app.py

app.py

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+import streamlit as st
+from transformers import ViltProcessor, ViltForQuestionAnswering, BlipForQuestionAnswering, AutoProcessor
+from PIL import Image
+
+# Define available models
+models = {
+    "ViLT": (ViltProcessor, ViltForQuestionAnswering, "dandelin/vilt-b32-finetuned-vqa"),
+    "BLIP": (AutoProcessor, BlipForQuestionAnswering, "Salesforce/blip-vqa-base"),
+}
+
+# Streamlit app
+st.title("Simple VQA App 🤖🎈")
+st.subheader("A demo app showcasing VQA models. ViLT and BLIP model.")
+# Sidebar for model selection
+selected_model = st.sidebar.selectbox("Select Model", list(models.keys()))
+
+# Load selected model and processor
+processor, model_class, model_name = models[selected_model]
+processor = processor.from_pretrained(model_name)
+model = model_class.from_pretrained(model_name)
+
+# Image and question input
+uploaded_image = st.file_uploader("Upload Image")
+question = st.text_input("Ask a Question about the Image")
+
+# Process image and question if provided
+if uploaded_image and question:
+    image = Image.open(uploaded_image)
+    st.image(image, caption="Uploaded Image")
+    encoding = processor(image, question, return_tensors="pt")
+    outputs = model(**encoding)
+    logits = outputs.logits
+    idx = logits.argmax(-1).item()
+    answer = model.config.id2label[idx]
+    # Display answer with animation emoji
+    st.write(f"🤔 Model Answer: {answer} 🎉")
+# Disclaimer
+st.sidebar.markdown("This is a demo app showcasing VQA models. Actual performance may vary.")