app.py

import streamlit as st
from PIL import Image
import torch
from torch import nn as nn
from torchvision.transforms import transforms



class CNNModel(nn.Module):
    def __init__(self):
        super(CNNModel, self).__init__()
        self.conv_layers = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, padding=0),
            nn.ReLU(),
            nn.BatchNorm2d(32),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(32, 64, kernel_size=3, padding=0),
            nn.ReLU(),
            nn.BatchNorm2d(64),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(64, 128, kernel_size=3, padding=0),
            nn.ReLU(),
            nn.BatchNorm2d(128),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.fc_layers = nn.Sequential(
            nn.Flatten(),
            nn.Linear(128 * 30 * 30, 128),
            nn.ReLU(),
            nn.Dropout(0.1),
            nn.Linear(128, 64),
            nn.ReLU(),
            nn.Dropout(0.1),
            nn.Linear(64, 1),
            nn.Sigmoid()
        )

    def forward(self, x):
        x = self.conv_layers(x)
        x = self.fc_layers(x)
        return x


def load_checkpoint(checkpoint, model):
    print("=> Loading checkpoint")
    model.load_state_dict(checkpoint["state_dict"])


model = CNNModel()
load_checkpoint(torch.load("emotion.pth.tar"), model)
model.eval()
class_names = ["angry", "disgust", "fear", "happy", "neutral", "sad", "surprize"]

st.title("Emotion Detector")
st.write("Upload an image and let the model predict your emotion!")

uploaded_image = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])

if uploaded_image is not None:
    image = Image.open(uploaded_image)
    st.image(image, caption="Uploaded Image", use_column_width=True)

    # Define the transformation to convert the image to a tensor
    transform = transforms.Compose([
        transforms.Resize((256, 256)),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    ])

    # Apply the transformation to the image
    tensor_image = transform(image)

    # Predict
    predictions = model(tensor_image.unsqueeze(0))
    predicted_class_index = torch.argmax(predictions).item()
    predicted_class = class_names[predicted_class_index]

    st.write(f"Prediction: {predicted_class}")