Create app.py

app.py

@@ -0,0 +1,30 @@
+import streamlit as st
+from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
+from PIL import Image
+
+# Load the pre-trained model and processor
+model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+processor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+
+# Streamlit app title
+st.title("Image to Text App")
+
+# File uploader
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Load and display the image
+    image = Image.open(uploaded_file)
+    st.image(image, caption='Uploaded Image', use_column_width=True)
+
+    # Process the image
+    pixel_values = processor(images=image, return_tensors="pt").pixel_values
+
+    # Generate text
+    output_ids = model.generate(pixel_values)
+    text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+
+    # Display the generated text
+    st.write("Generated Text:")
+    st.write(text)