Create app.py

app.py

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+import streamlit as st
+from transformers import ViltProcessor, ViltForQuestionAnswering
+from PIL import Image
+import torch
+
+# Load the VILT processor and model for visual question answering
+processor = ViltProcessor.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
+model = ViltForQuestionAnswering.from_pretrained("dandelin/vilt-b32-finetuned-vqa")
+
+# Streamlit app UI
+st.title("Visual Question Answering (VQA) with VILT")
+
+# Image uploader
+uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
+
+# Question input
+question = st.text_input("Enter your question about the image:")
+
+# A button to trigger the VQA task
+if st.button("Get Answer"):
+    if uploaded_image is None:
+        st.error("Please upload an image.")
+    elif question == "":
+        st.error("Please enter a question.")
+    else:
+        try:
+            # Load the image from the uploader
+            image = Image.open(uploaded_image)
+
+            # Show the uploaded image in the app
+            st.image(image, caption="Uploaded Image", use_column_width=True)
+
+            # Process the image and question
+            encoding = processor(image, question, return_tensors="pt")
+
+            # Forward pass through the model
+            outputs = model(**encoding)
+            logits = outputs.logits
+            idx = logits.argmax(-1).item()
+
+            # Get the predicted answer
+            answer = model.config.id2label[idx]
+
+            # Show the answer
+            st.success(f"Predicted Answer: {answer}")
+
+        except Exception as e:
+            st.error(f"Error: {str(e)}")