Upload app.py

Basic app with single image and PDF slide analysis functionality

app.py

@@ -0,0 +1,391 @@
+import os
+import sys
+import math
+import numpy as np
+import tempfile
+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
+import pdf2image
+
+# Constants
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+# Configuration
+MODEL_NAME = "OpenGVLab/InternVL2_5-8B"
+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
+        )
+        
+        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
+
+# Extract slides from uploaded PDF file
+def extract_slides_from_pdf(file_obj):
+    try:
+        file_bytes = file_obj.read()
+        file_extension = os.path.splitext(file_obj.name)[1].lower()
+        
+        # Check if it's a PDF
+        if file_extension != '.pdf':
+            return []
+        
+        # Create temporary file
+        with tempfile.NamedTemporaryFile(delete=False, suffix=file_extension) as temp_file:
+            temp_file.write(file_bytes)
+            temp_path = temp_file.name
+        
+        # Extract images from PDF using pdf2image
+        slides = []
+        try:
+            images = pdf2image.convert_from_path(temp_path, dpi=300)
+            slides = [(f"Slide {i+1}", img) for i, img in enumerate(images)]
+        except Exception as e:
+            print(f"Error converting PDF: {e}")
+        
+        # Clean up temporary file
+        os.unlink(temp_path)
+        
+        return slides
+    
+    except Exception as e:
+        import traceback
+        error_msg = f"Error extracting slides: {str(e)}\n{traceback.format_exc()}"
+        print(error_msg)
+        return []
+
+# 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
+
+# Analyze multiple slides from a PDF
+def analyze_pdf_slides(model, tokenizer, file_obj, prompt, num_slides=2):
+    try:
+        if file_obj is None:
+            return "Please upload a PDF file."
+        
+        # Extract slides from PDF
+        slides = extract_slides_from_pdf(file_obj)
+        
+        if not slides:
+            return "No slides were extracted from the file. Please check that it's a valid PDF."
+        
+        # Limit to the requested number of slides
+        slides = slides[:num_slides]
+        
+        # Analyze each slide
+        analyses = []
+        for slide_title, slide_image in slides:
+            analysis = analyze_image(model, tokenizer, slide_image, prompt)
+            analyses.append((slide_title, analysis))
+        
+        # Format the results
+        result = ""
+        for slide_title, analysis in analyses:
+            result += f"## {slide_title}\n\n{analysis}\n\n---\n\n"
+        
+        return result
+    
+    except Exception as e:
+        import traceback
+        error_msg = f"Error analyzing slides: {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 Analyzer - Error",
+            description="The model failed to load. Please check the logs for more information."
+        )
+        return demo
+    
+    # Create an interface with tabs
+    with gr.Blocks(title="InternVL2.5 Analyzer") as demo:
+        gr.Markdown("# InternVL2.5 Image and Slide Analyzer")
+        
+        with gr.Tabs():
+            # Single Image Analysis Tab
+            with gr.TabItem("Single Image Analysis"):
+                # Predefined prompts for analysis
+                image_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."
+                ]
+                
+                with gr.Row():
+                    image_input = gr.Image(type="pil", label="Upload Image")
+                    image_prompt = gr.Dropdown(
+                        choices=image_prompts, 
+                        value=image_prompts[0], 
+                        label="Select a prompt",
+                        allow_custom_value=True
+                    )
+                
+                image_analyze_btn = gr.Button("Analyze Image")
+                image_output = gr.Textbox(label="Analysis Results", lines=15)
+                
+                # Handle the image analysis action
+                image_analyze_btn.click(
+                    fn=lambda img, prompt: analyze_image(model, tokenizer, img, prompt),
+                    inputs=[image_input, image_prompt],
+                    outputs=image_output
+                )
+            
+            # PDF Slides Analysis Tab
+            with gr.TabItem("PDF Slides Analysis"):
+                slide_prompts = [
+                    "Analyze this slide and describe its contents.",
+                    "What is the main message of this slide?",
+                    "Extract all the text visible in this slide.",
+                    "What are the key points presented in this slide?",
+                    "Describe the visual elements and layout of this slide."
+                ]
+                
+                with gr.Row():
+                    file_input = gr.File(label="Upload PDF")
+                    slide_prompt = gr.Dropdown(
+                        choices=slide_prompts, 
+                        value=slide_prompts[0], 
+                        label="Select a prompt",
+                        allow_custom_value=True
+                    )
+                
+                num_slides = gr.Slider(
+                    minimum=1, 
+                    maximum=5, 
+                    value=2, 
+                    step=1, 
+                    label="Number of Slides to Analyze"
+                )
+                
+                slides_analyze_btn = gr.Button("Analyze Slides")
+                slides_output = gr.Markdown(label="Analysis Results")
+                
+                # Handle the slides analysis action
+                slides_analyze_btn.click(
+                    fn=lambda file, prompt, num: analyze_pdf_slides(model, tokenizer, file, prompt, num),
+                    inputs=[file_input, slide_prompt, num_slides],
+                    outputs=slides_output
+                )
+                
+                # Add example if available
+                if os.path.exists("example_slides/test_slides.pdf"):
+                    gr.Examples(
+                        examples=[
+                            ["example_slides/test_slides.pdf", "Extract all the text visible in this slide.", 2]
+                        ],
+                        inputs=[file_input, slide_prompt, num_slides]
+                    )
+    
+    return demo
+
+# Run the application
+if __name__ == "__main__":
+    try:
+        # 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()