Delete app.py

app.py

@@ -1,70 +0,0 @@
-
-import torch
-import torch.nn as nn
-import torchvision.transforms as transforms
-from PIL import Image
-import gradio as gr
-
-# Define your model class (must match the architecture used for training)
-class EmotionModel(nn.Module):
-    def __init__(self):
-        super(EmotionModel, self).__init__()
-        self.model = nn.Sequential(
-            nn.Conv2d(1, 32, 3, padding=1),   # First conv layer: 32 channels
-            nn.ReLU(),
-            nn.MaxPool2d(2, 2),
-
-            nn.Conv2d(32, 64, 3, padding=1),  # Second conv layer: 64 channels
-            nn.ReLU(),
-            nn.MaxPool2d(2, 2),
-
-            nn.Flatten(),
-            nn.Linear(64 * 12 * 12, 128),     # Matches flatten shape 64*12*12=9216
-            nn.ReLU(),
-            nn.Linear(128, 7)                 # 7 emotion classes
-        )
-
-    def forward(self, x):
-        return self.model(x)
-
-# Load model
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-model = EmotionModel()
-model.load_state_dict(torch.load("emotion_model.pth", map_location=device))
-model.eval()
-
-# Class labels (order must match training)
-EMOTIONS = ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
-
-# Image transform
-transform = transforms.Compose([
-    transforms.Grayscale(),
-    transforms.Resize((48, 48)),
-    transforms.ToTensor(),
-])
-
-# Prediction function
-def predict_emotion(img):
-    if img is None:
-        return "⚠️ No image received. Please upload or take a photo."
-
-    img = transform(img).unsqueeze(0).to(device)
-    with torch.no_grad():
-        output = model(img)
-        _, predicted = torch.max(output, 1)
-        return f"Predicted Emotion: {EMOTIONS[predicted.item()]}"
-
-# Gradio UI
-with gr.Blocks() as demo:
-    gr.Markdown("## 😊 Emotion Detection App")
-
-    with gr.Row():
-        webcam = gr.Image(type="pil", label="Upload or Take Photo")
-        output = gr.Textbox(label="Prediction")
-
-    with gr.Row():
-        predict_btn = gr.Button("Predict Emotion")
-
-    predict_btn.click(fn=predict_emotion, inputs=webcam, outputs=output)
-
-demo.launch()