Create app.py

app.py

@@ -0,0 +1,46 @@
+import gradio as gr
+from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
+from PIL import Image
+import torch
+
+# Load the pre-trained model, processor, 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")
+
+# Set the device to GPU if available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# Define generation parameters
+max_length = 16
+num_beams = 4
+
+# Function to generate caption from image
+def generate_caption(image):
+    if image is None:
+        return "Please upload an image."
+    # Convert image to RGB if it's not
+    if image.mode != "RGB":
+        image = image.convert(mode="RGB")
+    # Preprocess the image
+    pixel_values = feature_extractor(images=image, return_tensors="pt").pixel_values
+    pixel_values = pixel_values.to(device)
+    # Generate caption
+    output_ids = model.generate(pixel_values, max_length=max_length, num_beams=num_beams)
+    caption = tokenizer.decode(output_ids[0], skip_special_tokens=True)
+    return caption.strip()
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=generate_caption,
+    inputs=gr.Image(type="pil", label="Upload an Image"),
+    outputs=gr.Textbox(label="Generated Caption"),
+    title="🖼️ AI Image Caption Generator",
+    description="Upload an image, and the AI will generate a descriptive caption for it.",
+    allow_flagging="never"
+)
+
+# Launch the app
+if __name__ == "__main__":
+    iface.launch()