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Final year project Abstract 2023
The project is based on customer reviews on Amazon or Flipkart. The majority of internet reviews are based on technologies, pricing, specifications, prior to the sale of a product. While purchasing any product, a customer remains in doubt. Although he/she is familiar with specifications, he/she does not know anything about the product's quality or experience. It is important for a customer to know what other customers have experienced with the product. The main goal of this project is to get feedback/reviews from customers about the product and we can help our users with their recommendations, complaints, experiences, and qualities of other customers' experiences with the product. Natural language processing is used to extract sentiment from reviews and help new customers during the purchase process.
Stable Diffusion image pa
import torch from diffusers import StableDiffusionPipeline , EulerDiscreteScheduler import matplotlib.pyplot as plt from PIL import Image # Load the pipeline pipe = StableDiffusionPipeline.from_pretrained ( "CompVis/stable-diffusion-v1-4" , torch_dtype=torch.float16 ) pipe = pipe.to ( "cuda" ) # Set a scheduler to include callback pipe.scheduler = EulerDiscreteScheduler.from_config ( pipe.scheduler.config ) # Define the callback to capture intermediate images intermediate_images = [] def save_intermediate_images ( pipeline , step , timestep , extra_inputs ) : """ Callback function to save intermediate images. """ latents = extra_inputs [ "latents" ] # Retrieve the latents from the extra inputs if step == 0 : # Capture the first random latent (noise) with torch.no_grad (): random_latent_image = p...
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