Create app.py

app.py

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+import gradio as gr
+
+from transformers import LlavaNextForConditionalGeneration, LlavaNextProcessor
+
+from PIL import Image
+
+import requests
+
+import torch
+
+import spaces
+
+# Load the processor and model
+
+processor = LlavaNextProcessor.from_pretrained("tiiuae/falcon-11B-vlm", tokenizer_class='PreTrainedTokenizerFast')
+
+model = LlavaNextForConditionalGeneration.from_pretrained("tiiuae/falcon-11B-vlm", torch_dtype=torch.bfloat16).to('cuda:0')
+
+
+@spaces.GPU
+def generate_paragraph(image_url):
+
+    cats_image = Image.open(requests.get(image_url, stream=True).raw)
+
+    instruction = 'Write a long paragraph about this picture.'
+
+    
+
+    prompt = f"User:<image>\n{instruction} Falcon:"
+
+    inputs = processor(prompt, images=cats_image, return_tensors="pt", padding=True).to('cuda:0')
+
+    
+
+    output = model.generate(**inputs, max_new_tokens=256)
+
+    generated_captions = processor.decode(output[0], skip_special_tokens=True).strip()
+
+    
+
+    return generated_captions
+
+
+
+# Define the Gradio interface
+
+interface = gr.Interface(
+
+    fn=generate_paragraph,
+
+    inputs=gr.inputs.Textbox(label="Image URL"),
+
+    outputs=gr.outputs.Textbox(label="Generated Paragraph"),
+
+    title="Image to Paragraph Generation",
+
+    description="Enter the URL of an image, and the model will generate a descriptive paragraph about the image."
+
+)
+
+
+
+# Launch the Gradio interface
+
+interface.launch()