!pip install torch torchvision transformers timm pillow matplotlib opencv-python tensorflow-hub tensorflow!pip install huggingface_hub sentence-transformers ftfy regex tqdm!pip install accelerate

import torchprint(f"PyTorch version: {torch.__version__}")print(f"CUDA available: {torch.cuda.is_available()}")if torch.cuda.is_available():   print(f"CUDA device: {torch.cuda.get_device_name(0)}")

import torchfrom PIL import Imageimport requestsfrom transformers import CLIPProcessor, CLIPModelimport matplotlib.pyplot as pltimport numpy as np# Load model and processormodel_id = "openai/clip-vit-base-patch32"model = CLIPModel.from_pretrained(model_id)processor = CLIPProcessor.from_pretrained(model_id)# Function to get image embeddingsdef get_clip_image_embedding(image_path):   image = Image.open(image_path) if isinstance(image_path, str) else image_path   inputs = processor(images=image, return_tensors="pt")   with torch.no_grad():       image_features = model.get_image_features(**inputs)   return image_features# Function to perform zero-shot classificationdef classify_image_with_clip(image_path, categories):   image = Image.open(image_path) if isinstance(image_path, str) else image_path   inputs = processor(       text=categories,       images=image,       return_tensors="pt",       padding=True   )   with torch.no_grad():       outputs = model(**inputs)       logits_per_image = outputs.logits_per_image       probs = logits_per_image.softmax(dim=1)   # Return dict of categories and probabilities   return {categories[i]: probs[0][i].item() for i in range(len(categories))}# Example: Product categorizationurl = "https://images.unsplash.com/photo-1542291026-7eec264c27ff?q=80&w=1470&auto=format&fit=crop"image = Image.open(requests.get(url, stream=True).raw)product_categories = [   "sneakers", "formal shoes", "sandals", "boots",   "sports equipment", "casual wear", "luxury item"]results = classify_image_with_clip(image, product_categories)# Sort results by probabilitysorted_results = dict(sorted(results.items(), key=lambda x: x[1], reverse=True))# Display the image and classification resultsplt.figure(figsize=(12, 6))# Plot the image on the leftplt.subplot(1, 2, 1)plt.imshow(np.array(image))plt.title("Input Image")plt.axis("off")# Plot the classification results on the rightplt.subplot(1, 2, 2)categories = list(sorted_results.keys())scores = list(sorted_results.values())y_pos = np.arange(len(categories))plt.barh(y_pos, scores, align="center")plt.yticks(y_pos, categories)plt.xlabel("Probability")plt.title("CLIP Classification Results")plt.tight_layout()plt.show()# Also print results to consoleprint("Classification Results:")for category, score in sorted_results.items():   print(f"{category}: {score:.4f}")

import torchimport torchvision.transforms as Tfrom PIL import Imageimport numpy as npimport matplotlib.pyplot as pltfrom torch.nn import functional as Fimport requestsfrom io import BytesIO# Load DINOv2 modeldinov2_vits14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vits14')dinov2_vits14.eval()# Preprocess images for DINOv2transform = T.Compose([   T.Resize(256),   T.CenterCrop(224),   T.ToTensor(),   T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),])# Function to extract featuresdef extract_dinov2_features(image_path):   image = Image.open(image_path).convert('RGB') if isinstance(image_path, str) else image_path   img_tensor = transform(image).unsqueeze(0)   with torch.no_grad():       features = dinov2_vits14(img_tensor)   return features# Function to compute similarity between imagesdef compute_similarity(img1_path, img2_path):   feat1 = extract_dinov2_features(img1_path)   feat2 = extract_dinov2_features(img2_path)   # Normalize features   feat1 = F.normalize(feat1, dim=1)   feat2 = F.normalize(feat2, dim=1)   # Compute cosine similarity   similarity = torch.mm(feat1, feat2.transpose(0, 1)).item()   return similarity# Function to download image from URLdef download_image(url):   response = requests.get(url, stream=True)   return Image.open(BytesIO(response.content)).convert('RGB')# Function to visualize image pair with similarity scoredef visualize_similarity(img1_path, img2_path, title=None):   # Load images   if img1_path.startswith(('http://', 'https://')):       img1 = download_image(img1_path)   else:       img1 = Image.open(img1_path).convert('RGB')   if img2_path.startswith(('http://', 'https://')):       img2 = download_image(img2_path)   else:       img2 = Image.open(img2_path).convert('RGB')   # Compute similarity   similarity = compute_similarity(img1, img2)   # Create figure for visualization   fig, axes = plt.subplots(1, 2, figsize=(12, 6))   # Display images   axes[0].imshow(np.array(img1))   axes[0].set_title("Image 1")   axes[0].axis("off")   axes[1].imshow(np.array(img2))   axes[1].set_title("Image 2")   axes[1].axis("off")   # Add similarity score as figure title   fig_title = f"Similarity Score: {similarity:.4f}"   if title:       fig_title = f"{title}n{fig_title}"   fig.suptitle(fig_title, fontsize=16)   plt.tight_layout()   plt.show()   return similarity# Example: Use direct URLs instead of downloading files first# Sample sneaker images from Unsplashurl1 = "https://images.unsplash.com/photo-1560769629-975ec94e6a86?w=500"  # Red sneakerurl2 = "https://images.unsplash.com/photo-1600185365926-3a2ce3cdb9eb?w=500"  # White sneakerurl3 = "https://images.unsplash.com/photo-1491553895911-0055eca6402d?w=500"  # Another sneaker# Visualize pairs with similarity scoresprint("Comparing Product 1 and Product 2:")similarity_1_2 = visualize_similarity(url1, url2, "Red Sneaker vs White Sneaker")print("nComparing Product 1 and Product 3:")similarity_1_3 = visualize_similarity(url1, url3, "Red Sneaker vs Another Sneaker")print("nComparing Product 2 and Product 3:")similarity_2_3 = visualize_similarity(url2, url3, "White Sneaker vs Another Sneaker")# Print summary of all similaritiesprint("nSummary of Similarity Scores:")print(f"Similarity between product 1 and 2: {similarity_1_2:.4f}")print(f"Similarity between product 1 and 3: {similarity_1_3:.4f}")print(f"Similarity between product 2 and 3: {similarity_2_3:.4f}")

# Install required libraries for SAM!pip install git+https://github.com/facebookresearch/segment-anything.gitimport torchimport numpy as npimport matplotlib.pyplot as pltfrom segment_anything import sam_model_registry, SamPredictorimport cv2from PIL import Imageimport requests# Download SAM checkpoint!wget -q https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth# Load SAM modelsam = sam_model_registry["vit_h"](checkpoint="sam_vit_h_4b8939.pth")device = "cuda" if torch.cuda.is_available() else "cpu"sam.to(device)predictor = SamPredictor(sam)# Function to perform automatic segmentationdef segment_image(image_path):   # Load image   image = cv2.imread(image_path)   image_rgb = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)   # Set image for SAM   predictor.set_image(image_rgb)   # Generate automatic masks   masks, scores, logits = predictor.predict(       point_coords=None,       point_labels=None,       multimask_output=True,       box=None   )   return image_rgb, masks, scores# Function to visualize segmentation resultsdef visualize_segmentation(image, masks, scores, limit=5):   plt.figure(figsize=(15, 10))   # Display original image   plt.subplot(1, limit+1, 1)   plt.imshow(image)   plt.title("Original Image")   plt.axis('off')   # Display top masks   top_indices = np.argsort(scores)[-limit:][::-1]   for i, idx in enumerate(top_indices):       plt.subplot(1, limit+1, i+2)       plt.imshow(image)       plt.imshow(masks[idx], alpha=0.7, cmap='jet')       plt.title(f"Mask {i+1}nScore: {scores[idx]:.3f}")       plt.axis('off')   plt.tight_layout()   plt.show()# Example: Product segmentation for e-commerce!wget -q -O product_image.jpg "https://images.unsplash.com/photo-1525966222134-fcfa99b8ae77?w=800"image_rgb, masks, scores = segment_image("product_image.jpg")visualize_segmentation(image_rgb, masks, scores)# Business application: Calculate precise product measurementsdef calculate_object_dimensions(mask):   # Find contours in the mask   contours, _ = cv2.findContours((mask * 255).astype(np.uint8),                                  cv2.RETR_EXTERNAL,                                  cv2.CHAIN_APPROX_SIMPLE)   if not contours:       return None   # Get the largest contour   largest_contour = max(contours, key=cv2.contourArea)   # Get bounding rectangle   x, y, w, h = cv2.boundingRect(largest_contour)   # Calculate aspect ratio   aspect_ratio = w / h   # Calculate area in pixels   area_pixels = cv2.contourArea(largest_contour)   return {       'width': w,       'height': h,       'aspect_ratio': aspect_ratio,       'area_pixels': area_pixels   }# Apply to the highest scoring maskbest_mask_idx = np.argmax(scores)dimensions = calculate_object_dimensions(masks[best_mask_idx])print("Product Dimensions:")print(f"Width: {dimensions['width']} pixels")print(f"Height: {dimensions['height']} pixels")print(f"Aspect Ratio: {dimensions['aspect_ratio']:.2f}")print(f"Area: {dimensions['area_pixels']} square pixels")

from transformers import Blip2Processor, Blip2ForConditionalGenerationimport torchfrom PIL import Imageimport requestsimport matplotlib.pyplot as pltimport numpy as npfrom io import BytesIO# Load BLIP-2 modelprocessor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")model = Blip2ForConditionalGeneration.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16)if torch.cuda.is_available():   model = model.to("cuda")# Function to download image from URLdef download_image(url):   response = requests.get(url, stream=True)   return Image.open(BytesIO(response.content)).convert('RGB')# Function for image captioningdef generate_caption(image_path):   # Load image from path or URL   if isinstance(image_path, str):       if image_path.startswith(('http://', 'https://')):           image = download_image(image_path)       else:           image = Image.open(image_path).convert('RGB')   else:       image = image_path   inputs = processor(images=image, return_tensors="pt")   if torch.cuda.is_available():       inputs = {k: v.to("cuda") for k, v in inputs.items()}   generated_ids = model.generate(**inputs, max_new_tokens=50)   generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()   return generated_text# Function for visual question answeringdef visual_qa(image_path, question):   # Load image from path or URL   if isinstance(image_path, str):       if image_path.startswith(('http://', 'https://')):           image = download_image(image_path)       else:           image = Image.open(image_path).convert('RGB')   else:       image = image_path   # FIX: Properly format the question for the model   # BLIP-2 needs a specific prompt format for QA   prompt = f"Question: {question} Answer:"   inputs = processor(images=image, text=prompt, return_tensors="pt")   if torch.cuda.is_available():       inputs = {k: v.to("cuda") for k, v in inputs.items()}   generated_ids = model.generate(       **inputs,       max_new_tokens=30,       do_sample=False  # Use greedy decoding for more precise answers   )   answer = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()   # Remove the prompt part from the answer   answer = answer.replace(prompt, "").strip()   return answer# Function to visualize image with caption and QAdef visualize_product_analysis(image_path, questions=None):   # Load image   if isinstance(image_path, str):       if image_path.startswith(('http://', 'https://')):           image = download_image(image_path)       else:           image = Image.open(image_path).convert('RGB')   else:       image = image_path   # Generate caption   caption = generate_caption(image)   # Default questions if none provided   if questions is None:       questions = [           "What color is this product?",           "What material is this product made of?",           "What is the target demographic for this product?",           "What is a key feature of this product?"       ]   # Get answers   answers = []   for question in questions:       answer = visual_qa(image, question)       answers.append((question, answer))   # Create visualization   plt.figure(figsize=(12, 10))   # Display image   plt.subplot(2, 1, 1)   plt.imshow(np.array(image))   plt.title("Product Image", fontsize=14)   plt.axis('off')   # Display caption and Q&A   plt.subplot(2, 1, 2)   plt.axis('off')   text_content = f"Generated Description: {caption}nn"   text_content += "Product Analysis:n"   for q, a in answers:       text_content += f"Q: {q}nA: {a}nn"   plt.text(0.01, 0.99, text_content, transform=plt.gca().transAxes,            fontsize=12, verticalalignment='top', wrap=True)   plt.tight_layout()   plt.show()   return caption, answers# Business application: Automated product listingdef create_product_listing(image_path):   # Load image   if isinstance(image_path, str):       if image_path.startswith(('http://', 'https://')):           image = download_image(image_path)       else:           image = Image.open(image_path).convert('RGB')   else:       image = image_path   # Get basic caption   caption = generate_caption(image)   # Extract product attributes with more specific prompting   color = visual_qa(image, "What colors are visible in this product?")   material = visual_qa(image, "What material does this product appear to be made of?")   use_case = visual_qa(image, "What would be the main use case for this product?")   unique_features = visual_qa(image, "What are any unique or notable features of this product?")   # Create structured listing   listing = {       "title": caption,       "attributes": {           "color": color,           "material": material,           "primary_use": use_case,           "unique_features": unique_features       }   }   # Visualize the listing   plt.figure(figsize=(14, 10))   # Display image   plt.subplot(1, 2, 1)   plt.imshow(np.array(image))   plt.title("Product Image", fontsize=14)   plt.axis('off')   # Display listing details   plt.subplot(1, 2, 2)   plt.axis('off')   listing_text = f"PRODUCT LISTINGnn"   listing_text += f"Title: {listing['title']}nn"   listing_text += "Product Attributes:n"   for attr, value in listing['attributes'].items():       listing_text += f"{attr.replace('_', ' ').title()}: {value}n"   plt.text(0.01, 0.99, listing_text, transform=plt.gca().transAxes,            fontsize=12, verticalalignment='top')   plt.tight_layout()   plt.show()   return listing# Function for marketing content analysisdef analyze_marketing_content(image_path):   # Load image   if isinstance(image_path, str):       if image_path.startswith(('http://', 'https://')):           image = download_image(image_path)       else:           image = Image.open(image_path).convert('RGB')   else:       image = image_path   # Marketing-specific questions   marketing_questions = [       "What emotions does this image evoke?",       "What brand values are communicated in this image?",       "What target audience would this image appeal to?",       "What call to action would pair well with this image?",       "What marketing channel would this image be most effective on?"   ]   # Get answers   marketing_insights = {}   for question in marketing_questions:       answer = visual_qa(image, question)       key = question.split("?")[0].strip().lower().replace(" ", "_")       marketing_insights[key] = answer   # Visualize the analysis   plt.figure(figsize=(14, 10))   # Display image   plt.subplot(1, 2, 1)   plt.imshow(np.array(image))   plt.title("Marketing Visual", fontsize=14)   plt.axis('off')   # Display marketing insights   plt.subplot(1, 2, 2)   plt.axis('off')   insights_text = "MARKETING CONTENT ANALYSISnn"   for question, key in zip(marketing_questions, marketing_insights.keys()):       insights_text += f"{question}n{marketing_insights[key]}nn"   plt.text(0.01, 0.99, insights_text, transform=plt.gca().transAxes,            fontsize=12, verticalalignment='top')   plt.tight_layout()   plt.show()   return marketing_insights# Function for social media understandingdef analyze_social_media_content(image_path):   # Load image   if isinstance(image_path, str):       if image_path.startswith(('http://', 'https://')):           image = download_image(image_path)       else:           image = Image.open(image_path).convert('RGB')   else:       image = image_path   # Generate caption   caption = generate_caption(image)   # Social media specific analysis   engagement_potential = visual_qa(image, "How likely is this image to engage viewers on social media?")   suggested_hashtags = visual_qa(image, "What hashtags would be appropriate for this image on social media?")   platform_fit = visual_qa(image, "Which social media platform would this image perform best on?")   content_type = visual_qa(image, "What type of social media post would this image be suitable for?")   # Create analysis dict   social_analysis = {       "caption": caption,       "engagement_potential": engagement_potential,       "suggested_hashtags": suggested_hashtags,       "platform_fit": platform_fit,       "content_type": content_type   }   # Visualize the analysis   plt.figure(figsize=(14, 10))   # Display image   plt.subplot(1, 2, 1)   plt.imshow(np.array(image))   plt.title("Social Media Content", fontsize=14)   plt.axis('off')   # Display social media insights   plt.subplot(1, 2, 2)   plt.axis('off')   insights_text = "SOCIAL MEDIA CONTENT ANALYSISnn"   insights_text += f"Caption: {social_analysis['caption']}nn"   insights_text += f"Engagement Potential: {social_analysis['engagement_potential']}nn"   insights_text += f"Suggested Hashtags: {social_analysis['suggested_hashtags']}nn"   insights_text += f"Best Platform: {social_analysis['platform_fit']}nn"   insights_text += f"Content Type: {social_analysis['content_type']}n"   plt.text(0.01, 0.99, insights_text, transform=plt.gca().transAxes,            fontsize=12, verticalalignment='top')   plt.tight_layout()   plt.show()   return social_analysis# Example usageif __name__ == "__main__":   # Example: E-commerce product analysis   product_url = "https://images.unsplash.com/photo-1598033129183-c4f50c736f10?w=800"   print("1. Basic Product Analysis")   caption, qa_results = visualize_product_analysis(product_url)   print("n2. Creating Automated Product Listing")   product_listing = create_product_listing(product_url)   print("n3. Marketing Content Analysis")   marketing_url = "https://images.unsplash.com/photo-1581252584837-9f0b1d3bf82c?ixlib=rb-4.0.3&q=80"   marketing_insights = analyze_marketing_content(marketing_url)   print("n4. Social Media Content Analysis")   social_url = "https://images.unsplash.com/photo-1534442072653-dbbf80c5e1ae?ixlib=rb-4.0.3&q=80"   social_analysis = analyze_social_media_content(social_url)