Create app.py

app.py

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+import gradio as gr
+from PIL import Image
+import torch
+from transformers import AutoProcessor
+
+# Model and processor initialization
+DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
+processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
+model = torch.load("../finetunned_blipv2_epoch_5_loss_0.4936.pth").to(DEVICE)
+model.eval()
+
+
+def caption_image(image: Image.Image) -> str:
+    """
+    Takes in an image and returns its caption using the trained model.
+    """
+    image = image.convert("RGB")
+    inputs = processor(images=image, return_tensors="pt").to(DEVICE)
+    pixel_values = inputs.pixel_values
+
+    with torch.no_grad():
+        generated_ids = model.generate(
+            pixel_values=pixel_values,
+            max_length=256
+        )
+
+    generated_caption = processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+    return generated_caption
+
+
+# Gradio interface
+interface = gr.Interface(
+    fn=caption_image,  # function to call
+    inputs=gr.Image(type="pil"),  # Image input
+    outputs=gr.Textbox(),  # Textbox output
+    title="Image Captioning with BLIP-2 and LoRa",
+    description=("<div style='text-align: center; padding: 10px; border: 2px solid #FFC107; border-radius: 10px;'>"
+                 "<p>Welcome to our <strong>state-of-the-art</strong> image captioning tool!</p>"
+                 "<p>We combine the strengths of the <em>BLIP-2</em> model with <em>LoRa</em> to provide precise image captions.</p>"
+                 "<p>Our rich dataset has been labeled using multi-modal models. Upload an image to see its caption!</p></div>"),
+    article=("<div style='text-align: center; padding: 10px; background-color: #E3F2FD; border-radius: 10px;'>"
+             "<a href='https://diegobonilla98.github.io/PixLore/' style='color: #1976D2; font-weight: bold;'>GitHub Project</a></div>"),
+    live=True,
+    layout="vertical"
+)
+
+
+if __name__ == '__main__':
+    interface.launch()