Update app.py

app.py

@@ -3,6 +3,8 @@ import torch
 from transformers import AutoModelForCausalLM
 from deepseek_vl.models import VLChatProcessor, MultiModalityCausalLM
 from deepseek_vl.utils.io import load_pil_images
+from io import BytesIO
+from PIL import Image
 import spaces  # Import spaces for ZeroGPU support
 
 # Load the model and processor
@@ -10,57 +12,77 @@ 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
+# Define the function for image description with ZeroGPU support
 @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
+def describe_image(image, user_question="Describe each stage of this image."):
+    try:
+        # Convert the PIL Image to a BytesIO object for compatibility
+        image_byte_arr = BytesIO()
+        image.save(image_byte_arr, format="PNG")  # Save image in PNG format
+        image_byte_arr.seek(0)  # Move pointer to the start
+
+        # Define the conversation, using the user's question
+        conversation = [
+            {
+                "role": "User",
+                "content": f"<image_placeholder>{user_question}",
+                "images": [image_byte_arr]  # Pass the image byte array instead of an object
+            },
+            {
+                "role": "Assistant",
+                "content": ""
+            }
+        ]
+
+        # Convert image byte array back to a PIL image for processing
+        pil_images = [Image.open(BytesIO(image_byte_arr.read()))]  # Convert byte back to PIL Image
+        image_byte_arr.seek(0)  # Reset the byte stream again for reuse
+
+        # Load images and prepare the inputs
+        prepare_inputs = vl_chat_processor(
+            conversations=conversation,
+            images=pil_images,
+            force_batchify=True
+        ).to('cuda')
+
+        # Load and prepare the model
+        vl_gpt = AutoModelForCausalLM.from_pretrained(model_path, trust_remote_code=True).to(torch.bfloat16).cuda().eval()
+
+        # Generate embeddings from the image input
+        inputs_embeds = vl_gpt.prepare_inputs_embeds(**prepare_inputs)
+
+        # Generate the model's response
+        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
+
+    except Exception as e:
+        # Provide detailed error information
+        return f"Error: {str(e)}"
 
 # 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.")
+        gr.Markdown("# Image Description with DeepSeek VL 1.3b\n### Upload an image and ask a question about it.")
 
         with gr.Row():
             image_input = gr.Image(type="pil", label="Upload an Image")
+            question_input = gr.Textbox(
+                label="Question (optional)", 
+                placeholder="Enter your question about the image (default: 'Describe each stage of this image.')",
+                lines=2
+            )
 
         output_text = gr.Textbox(label="Image Description", interactive=False)
 
@@ -68,7 +90,7 @@ def gradio_app():
 
         submit_btn.click(
             fn=describe_image,
-            inputs=[image_input],
+            inputs=[image_input, question_input],  # Pass both image and question as inputs
             outputs=output_text
         )