Delete untitled1(1).py

untitled1(1).py

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-# -*- coding: utf-8 -*-
-"""Untitled1.ipynb
-
-Automatically generated by Colab.
-
-Original file is located at
-    https://colab.research.google.com/drive/1kUKZzUV2R2HnFLLFwS8DE8pcQk6sbvY0
-"""
-
-
-
-# pretrained Resnet-18 mode
-import torch
-model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
-
-# define a function that takes in the user input, which in this case is an image, and returns the prediction.
-'''The prediction should be returned as a dictionary whose keys are class name and values are confidence probabilities.
-We will load the class names from this text file.
-'''
-!pip install transformers
-!pip install gradio
-
-import gradio as gr
-from transformers import BlipProcessor, BlipForConditionalGeneration
-from PIL import Image
-
-import requests
-from PIL import Image
-from torchvision import transforms
-
-# Download human-readable labels for ImageNet.
-response = requests.get("https://git.io/JJkYN")
-labels = response.text.split("\n")
-
-def predict(inp):
- inp = transforms.ToTensor()(inp).unsqueeze(0)
- with torch.no_grad():
-  prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
-  confidences = {labels[i]: float(prediction[i]) for i in range(1000)}
- return confidences
-
- '''The function converts the input image into a PIL Image and subsequently into a PyTorch tensor.
- After processing the tensor through the model, it returns the predictions in the form of a dictionary named confidences.
- The dictionary's keys are the class labels, and its values are the corresponding confidence probabilities.
-
-In this section, we define a predict function that processes an input image to return prediction probabilities.
-The function first converts the image into a PyTorch tensor and then forwards it through the pretrained model.
-
-We use the softmax function in the final step to calculate the probabilities of each class.
-The softmax function is crucial because it converts the raw output logits from the model, which can be any real number, into probabilities that sum up to 1.
-This makes it easier to interpret the model’s outputs as confidence levels for each class.'''
-
-# Creating a Gradio interface
-import gradio as gr
-
-gr.Interface(fn=predict,
-       inputs=gr.Image(type="pil"),             # creates the component and handles the preprocessing to convert that to a PIL image
-       outputs=gr.Label(num_top_classes=3),     # a Label, which displays the top labels in a nice form. Since we don't want to show all 1,000 class labels, we will customize it to show only the top 3 images by constructing it as
-       examples=["/lion.jpg", "/cheetah.jpg"]).launch()