made new file

app.py

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+import streamlit as st
+from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
+import torch
+from PIL import Image
+
+# Load pre-trained models and tokenizer
+model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+feature_extractor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
+
+# Check device and move model to the appropriate device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# Set generation parameters
+max_length = 16
+num_beams = 4
+gen_kwargs = {"max_length": max_length, "num_beams": num_beams}
+
+# Define the prediction function
+def predict_caption(image):
+    if image.mode != "RGB":
+        image = image.convert(mode="RGB")
+
+    # Process image and move pixel values to device
+    pixel_values = feature_extractor(images=[image], return_tensors="pt").pixel_values.to(device)
+    
+    # Generate caption
+    output_ids = model.generate(pixel_values, **gen_kwargs)
+    preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True)
+    
+    # Return the caption text
+    return preds[0].strip()
+
+# Main function for Streamlit app
+def main():
+    st.title("Image Caption Generator")
+    st.write("Upload an image, and the model will describe what it sees.")
+
+    # Upload image
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+    if uploaded_file is not None:
+        # Load and display the uploaded image
+        image = Image.open(uploaded_file)
+        st.image(image, caption='Uploaded Image', use_column_width=True)
+
+        # Generate and display caption
+        caption = predict_caption(image)
+        st.write("Caption:", caption)
+
+# Run the application
+if __name__ == "__main__":
+    main()