Create app.py

app.py

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+import gradio as gr
+import torch
+from PIL import Image
+from lavis.models import load_model_and_preprocess
+import json
+
+# Load the Blip-Caption model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model, vis_processors, _ = load_model_and_preprocess(name="blip_caption", model_type="base_coco", is_eval=True, device=device)
+
+# Define the input and output functions for Gradio
+def generate_caption(image_file):
+        # Load the image from the file path
+        raw_image = Image.open(image_file).convert("RGB")
+
+        # Preprocess the image using the Blip-Caption model's visual processors
+        image = vis_processors["eval"](raw_image).unsqueeze(0).to(device)
+
+        # Generate captions using the Blip-Caption model
+        captions = model.generate({"image": image}, use_nucleus_sampling=True, num_captions=5)
+        res=" "
+        for i in captions:
+          res=res+", "+i
+        return (res)
+
+# Set up the Gradio interface
+inputs = gr.inputs.Image(type="pil",label="Image")
+outputs = gr.Textbox(label="Captions")
+interface = gr.Interface(fn=generate_caption, inputs=inputs, outputs="text", title="Blip-Caption")
+
+# Launch the interface
+interface.launch(share=True)