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app.py

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+import os
+import sys
+import math
+import numpy as np
+import torch
+import torchvision.transforms as T
+from torchvision.transforms.functional import InterpolationMode
+from PIL import Image
+import gradio as gr
+from transformers import AutoModel, AutoTokenizer
+
+# Constants
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+# Configuration
+MODEL_NAME = "OpenGVLab/InternVL2_5-8B"  # Smaller model for faster loading
+IMAGE_SIZE = 448
+
+# Set up environment variables
+os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "max_split_size_mb:128"
+
+# Utility functions for image processing
+def build_transform(input_size):
+    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
+    transform = T.Compose([
+        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
+        T.ToTensor(),
+        T.Normalize(mean=MEAN, std=STD)
+    ])
+    return transform
+
+def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
+    best_ratio_diff = float('inf')
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff:
+            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+                best_ratio = ratio
+    return best_ratio
+
+def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+
+    # calculate the existing image aspect ratio
+    target_ratios = set(
+        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
+        i * j <= max_num and i * j >= min_num)
+    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio, target_ratios, orig_width, orig_height, image_size)
+
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = (
+            (i % (target_width // image_size)) * image_size,
+            (i // (target_width // image_size)) * image_size,
+            ((i % (target_width // image_size)) + 1) * image_size,
+            ((i // (target_width // image_size)) + 1) * image_size
+        )
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+    assert len(processed_images) == blocks
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+    return processed_images
+
+# Function to split model across GPUs
+def split_model(model_name):
+    device_map = {}
+    world_size = torch.cuda.device_count()
+    if world_size <= 1:
+        return "auto"
+    
+    num_layers = {
+        'InternVL2_5-1B': 24, 
+        'InternVL2_5-2B': 24, 
+        'InternVL2_5-4B': 36, 
+        'InternVL2_5-8B': 32,
+        'InternVL2_5-26B': 48, 
+        'InternVL2_5-38B': 64, 
+        'InternVL2_5-78B': 80
+    }[model_name]
+    
+    # Since the first GPU will be used for ViT, treat it as half a GPU.
+    num_layers_per_gpu = math.ceil(num_layers / (world_size - 0.5))
+    num_layers_per_gpu = [num_layers_per_gpu] * world_size
+    num_layers_per_gpu[0] = math.ceil(num_layers_per_gpu[0] * 0.5)
+    layer_cnt = 0
+    for i, num_layer in enumerate(num_layers_per_gpu):
+        for j in range(num_layer):
+            device_map[f'language_model.model.layers.{layer_cnt}'] = i
+            layer_cnt += 1
+    device_map['vision_model'] = 0
+    device_map['mlp1'] = 0
+    device_map['language_model.model.tok_embeddings'] = 0
+    device_map['language_model.model.embed_tokens'] = 0
+    device_map['language_model.model.rotary_emb'] = 0
+    device_map['language_model.output'] = 0
+    device_map['language_model.model.norm'] = 0
+    device_map['language_model.lm_head'] = 0
+    device_map[f'language_model.model.layers.{num_layers - 1}'] = 0
+
+    return device_map
+
+# Model loading function
+def load_model():
+    print(f"\n=== Loading {MODEL_NAME} ===")
+    print(f"CUDA available: {torch.cuda.is_available()}")
+    
+    if torch.cuda.is_available():
+        print(f"GPU count: {torch.cuda.device_count()}")
+        for i in range(torch.cuda.device_count()):
+            print(f"GPU {i}: {torch.cuda.get_device_name(i)}")
+            
+        # Memory info
+        print(f"Total GPU memory: {torch.cuda.get_device_properties(0).total_memory / 1e9:.2f} GB")
+        print(f"Allocated GPU memory: {torch.cuda.memory_allocated() / 1e9:.2f} GB")
+        print(f"Reserved GPU memory: {torch.cuda.memory_reserved() / 1e9:.2f} GB")
+    
+    # Determine device map
+    device_map = "auto"
+    if torch.cuda.is_available() and torch.cuda.device_count() > 1:
+        model_short_name = MODEL_NAME.split('/')[-1]
+        device_map = split_model(model_short_name)
+    
+    # Load model and tokenizer
+    try:
+        model = AutoModel.from_pretrained(
+            MODEL_NAME,
+            torch_dtype=torch.bfloat16 if torch.cuda.is_available() else torch.float32,
+            low_cpu_mem_usage=True,
+            trust_remote_code=True,
+            device_map=device_map
+        )
+        
+        tokenizer = AutoTokenizer.from_pretrained(
+            MODEL_NAME,
+            use_fast=False,
+            trust_remote_code=True
+        )
+        
+        # Fix for image context token ID - needed to make the model work with images
+        print("Setting image context token ID...")
+        if hasattr(tokenizer, 'encode'):
+            # Get special token ID from tokenizer
+            img_context_token_id = tokenizer.encode("<image>", add_special_tokens=False)[0]
+            model.img_context_token_id = img_context_token_id
+            print(f"Set img_context_token_id to {img_context_token_id}")
+        
+        print(f"✓ Model and tokenizer loaded successfully!")
+        return model, tokenizer
+    except Exception as e:
+        print(f"❌ Error loading model: {e}")
+        import traceback
+        traceback.print_exc()
+        return None, None
+
+# Image analysis function
+def analyze_image(model, tokenizer, image, prompt):
+    try:
+        # Check if image is valid
+        if image is None:
+            return "Please upload an image first."
+        
+        # Process the image
+        processed_images = dynamic_preprocess(image, image_size=IMAGE_SIZE)
+        
+        # Prepare the prompt
+        text_prompt = f"USER: <image>\n{prompt}\nASSISTANT:"
+        
+        # Convert inputs for the model
+        inputs = tokenizer([text_prompt], return_tensors="pt")
+        
+        # Move inputs to the right device
+        if torch.cuda.is_available():
+            inputs = {k: v.cuda() for k, v in inputs.items()}
+        
+        # Add image to the inputs
+        inputs["images"] = processed_images
+        
+        # Generate a response
+        with torch.no_grad():
+            outputs = model.generate(
+                **inputs,
+                max_new_tokens=512,
+            )
+        
+        # Decode the outputs
+        generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        # Extract only the assistant's response
+        assistant_response = generated_text.split("ASSISTANT:")[-1].strip()
+        
+        return assistant_response
+    except Exception as e:
+        import traceback
+        error_msg = f"Error analyzing image: {str(e)}\n{traceback.format_exc()}"
+        return error_msg
+
+# Function to handle two images
+def analyze_two_images(model, tokenizer, image1, image2, prompt):
+    try:
+        # Check if at least one image is provided
+        if image1 is None and image2 is None:
+            return "Please upload at least one image."
+        
+        results = []
+        
+        # Process first image if provided
+        if image1 is not None:
+            result1 = analyze_image(model, tokenizer, image1, prompt)
+            results.append(f"# Image 1 Analysis\n\n{result1}")
+        else:
+            results.append("# Image 1\n\nNo image uploaded.")
+        
+        # Process second image if provided
+        if image2 is not None:
+            result2 = analyze_image(model, tokenizer, image2, prompt)
+            results.append(f"# Image 2 Analysis\n\n{result2}")
+        else:
+            results.append("# Image 2\n\nNo image uploaded.")
+        
+        # Combine results
+        combined_result = f"{results[0]}\n\n---\n\n{results[1]}"
+        
+        return combined_result
+    except Exception as e:
+        import traceback
+        error_msg = f"Error analyzing images: {str(e)}\n{traceback.format_exc()}"
+        return error_msg
+
+# Main function
+def main():
+    # Load the model
+    model, tokenizer = load_model()
+    
+    if model is None:
+        # Create an error interface if model loading failed
+        demo = gr.Interface(
+            fn=lambda x: "Model loading failed. Please check the logs for details.",
+            inputs=gr.Textbox(),
+            outputs=gr.Textbox(),
+            title="InternVL2.5 Dual Image Analyzer - Error",
+            description="The model failed to load. Please check the logs for more information."
+        )
+        return demo
+    
+    # Predefined prompts for analysis
+    prompts = [
+        "Describe this image in detail.",
+        "What can you tell me about this image?",
+        "Is there any text in this image? If so, can you read it?",
+        "What is the main subject of this image?", 
+        "What emotions or feelings does this image convey?",
+        "Describe the composition and visual elements of this image.",
+        "Summarize what you see in this image in one paragraph.",
+        "Compare these images and describe the differences."
+    ]
+    
+    # Create the interface with two images
+    with gr.Blocks(title="InternVL2.5 Dual Image Analyzer") as demo:
+        gr.Markdown("# 🖼️ InternVL2.5 Dual Image Analyzer")
+        gr.Markdown("Upload one or two images and ask the InternVL2.5 model to analyze them.")
+        
+        with gr.Row():
+            with gr.Column(scale=1):
+                image1 = gr.Image(type="pil", label="Upload Image 1")
+                image2 = gr.Image(type="pil", label="Upload Image 2")
+                prompt = gr.Dropdown(
+                    choices=prompts, 
+                    value=prompts[0], 
+                    label="Select a prompt or write your own below",
+                    allow_custom_value=True
+                )
+                analyze_button = gr.Button("Analyze Images", variant="primary")
+                
+            with gr.Column(scale=1):
+                output = gr.Markdown(label="Analysis Results")
+        
+        analyze_button.click(
+            fn=lambda img1, img2, p: analyze_two_images(model, tokenizer, img1, img2, p),
+            inputs=[image1, image2, prompt],
+            outputs=output
+        )
+        
+        # Example images
+        if os.path.exists("example_images"):
+            example_files = [f for f in os.listdir("example_images") if f.endswith((".jpg", ".jpeg", ".png"))]
+            if len(example_files) >= 2:
+                example1 = os.path.join("example_images", example_files[0])
+                example2 = os.path.join("example_images", example_files[1])
+                
+                examples = [
+                    [example1, None, "Describe this image in detail."],
+                    [None, example2, "Describe this image in detail."],
+                    [example1, example2, "Compare these images and describe the differences."]
+                ]
+                
+                gr.Examples(
+                    examples=examples,
+                    inputs=[image1, image2, prompt]
+                )
+    
+    return demo
+
+# Run the application
+if __name__ == "__main__":
+    try:
+        # Check for GPU
+        if not torch.cuda.is_available():
+            print("WARNING: CUDA is not available. The model requires a GPU to function properly.")
+            
+        # Create and launch the interface
+        demo = main()
+        demo.launch(server_name="0.0.0.0")
+    except Exception as e:
+        print(f"Error starting the application: {e}")
+        import traceback
+        traceback.print_exc()