Create app.py

app.py

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+import gradio as gr
+import torch
+from transformers import AutoModelForCausalLM
+from deepseek_vl.models import VLChatProcessor, MultiModalityCausalLM
+from deepseek_vl.utils.io import load_pil_images
+import spaces  # Import spaces for ZeroGPU support
+
+# Load the model and processor
+model_path = "deepseek-ai/deepseek-vl-1.3b-chat"
+vl_chat_processor = VLChatProcessor.from_pretrained(model_path)
+tokenizer = vl_chat_processor.tokenizer
+
+# Define the function for image description
+@spaces.GPU  # Ensures GPU allocation for this function
+def describe_image(image):
+    # Define the conversation
+    conversation = [
+        {
+            "role": "User",
+            "content": "<image_placeholder>Describe this image in great detail.",
+            "images": [image]
+        },
+        {
+            "role": "Assistant",
+            "content": ""
+        }
+    ]
+
+    # Load image and process inputs
+    pil_images = load_pil_images(conversation)
+    prepare_inputs = vl_chat_processor(
+        conversations=conversation,
+        images=pil_images,
+        force_batchify=True
+    ).to('cuda')
+
+    # Run the image encoder to get embeddings
+    vl_gpt = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True).to(torch.bfloat16).cuda().eval()
+    inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)
+
+    # Generate response from the model
+    outputs = vl_gpt.language_model.generate(
+        inputs_embeds=inputs_embeds,
+        attention_mask=prepare_inputs.attention_mask,
+        pad_token_id=tokenizer.eos_token_id,
+        bos_token_id=tokenizer.bos_token_id,
+        eos_token_id=tokenizer.eos_token_id,
+        max_new_tokens=512,
+        do_sample=False,
+        use_cache=True
+    )
+
+    # Decode the generated tokens into text
+    answer = tokenizer.decode(outputs[0].cpu().tolist(), skip_special_tokens=True)
+    return answer
+
+# Gradio interface
+def gradio_app():
+    with gr.Blocks() as demo:
+        gr.Markdown("# Image Description with DeepSeek VL 1.3b\n### Upload an image to receive a detailed description.")
+
+        with gr.Row():
+            image_input = gr.Image(type="pil", label="Upload an Image")
+
+        output_text = gr.Textbox(label="Image Description", interactive=False)
+
+        submit_btn = gr.Button("Generate Description")
+
+        submit_btn.click(
+            fn=describe_image,
+            inputs=[image_input],
+            outputs=output_text
+        )
+
+    demo.launch()
+
+# Launch the Gradio app
+gradio_app()