Create app.py

app.py

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+import gradio as gr
+import torch
+import requests
+from PIL import Image
+from transformers import BlipProcessor, BlipForConditionalGeneration
+
+processor = BlipProcessor.from_pretrained("Salesforce/blip-image-captioning-large")
+model = BlipForConditionalGeneration.from_pretrained("Salesforce/blip-image-captioning-large", torch_dtype=torch.float16).to("cuda")
+
+def infer(image_input):
+    #img_url = 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/demo.jpg' 
+    raw_image = Image.open(requests.get(image_input, stream=True).raw).convert('RGB')
+
+    # unconditional image captioning
+    inputs = processor(raw_image, return_tensors="pt").to("cuda", torch.float16)
+
+    out = model.generate(**inputs)
+
+    caption = processor.decode(out[0], skip_special_tokens=True)
+    print(caption)
+
+    return caption
+
+css="""
+#col-container {max-width: 910px; margin-left: auto; margin-right: auto;}
+a {text-decoration-line: underline; font-weight: 600;}
+"""
+
+with gr.Blocks(css=css) as demo:
+    with gr.Column(elem_id="col-container"):
+        gr.Markdown(
+            """
+            # Image to Story
+            Upload an image, get a story ! 
+            <br/>
+            <br/>
+            [![Duplicate this Space](https://huggingface.co/datasets/huggingface/badges/raw/main/duplicate-this-space-sm.svg)](https://huggingface.co/spaces/fffiloni/SplitTrack2MusicGen?duplicate=true) for longer audio, more control and no queue.</p>
+            """
+        )
+        image_in = gr.Image(label="Image input")
+        submit_btn = gr.Button('Sumbit')
+        story = gr.Textbox(label="Generated Story")
+    submit_btn.click(fn=infer, inputs=[image_in], outputs=[story])
+
+demo.queue().launch()