Delete app.py

app.py

@@ -1,65 +0,0 @@
-#!/usr/bin/env python
-# coding: utf-8
-# Save this file as streamlit.py and run it using the command: streamlit run streamlit.py
-
-import streamlit as st
-import torch
-from torch import nn
-from PIL import Image
-import numpy as np
-import torchvision.transforms as transforms
-
-# Define the model class exactly as it was during training if necessary
-class CustomModel(nn.Module):
-    def __init__(self):
-        super(CustomModel, self).__init__()
-        # Define layers here exactly as they were in the training script
-        # For example:
-        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
-        self.relu = nn.ReLU()
-        self.fc = nn.Linear(32 * 224 * 224, 90)  # Adjust the dimensions and number of classes if necessary
-
-    def forward(self, x):
-        x = self.conv1(x)
-        x = self.relu(x)
-        x = x.view(x.size(0), -1)  # Flatten the tensor
-        x = self.fc(x)
-        return x
-
-# Load the model
-model = CustomModel()
-model.load_state_dict(torch.load('model-CNN.pth'))  # or model-CNN.pt if saved with state_dict
-model.eval()
-
-# Function to process the image
-def process_image(img):
-    transform = transforms.Compose([
-        transforms.Resize((224, 224)),
-        transforms.ToTensor(),
-        transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
-    ])
-    img = transform(img)
-    img = img.unsqueeze(0)  # Add batch dimension
-    return img
-
-st.title('Animal Classification')
-st.write('Please choose an image so that the AI model can predict the type of animal.')
-file = st.file_uploader('Pick an image', type=['jpg', 'jpeg', 'png'])
-
-# Load animal names
-with open("name of the animals.txt") as f:
-    class_names = [x.strip() for x in f.readlines()]
-
-if file is not None:
-    img = Image.open(file)
-    st.image(img, caption='The image: ')
-    image = process_image(img)
-    
-    # Predict with the model
-    with torch.no_grad():
-        prediction = model(image)
-    
-    predicted_class = torch.argmax(prediction, dim=1).item()
-    st.write('Probability Distribution')
-    st.write(prediction.numpy())
-    st.write("Prediction: ", class_names[predicted_class])