Create app.py

app.py

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+import os
+import torch
+import torch.nn as nn
+import torch.optim as optim
+from torchvision import transforms, models
+from PIL import Image
+import gradio as gr
+import matplotlib.pyplot as plt
+import numpy as np
+
+# Load the pre-trained model (ensure to use the saved model checkpoint)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Model: EfficientNet-B0 with dropout added to reduce overfitting
+model = models.efficientnet_b0(pretrained=True)
+model.classifier = nn.Sequential(
+    nn.Dropout(0.4),
+    nn.Linear(model.classifier[1].in_features, 7)  # num_classes = 7 (angry, disgust, fear, happy, neutral, sad, surprise)
+)
+model.load_state_dict(torch.load("D:/Dataset/MMAFEDB/best_mood_classifier.pth"))
+model = model.to(device)
+model.eval()
+
+# Define the image transformations for the uploaded image
+transform = transforms.Compose([
+    transforms.Resize((224, 224)),
+    transforms.ToTensor(),
+    transforms.Normalize([0.485, 0.456, 0.406],
+                         [0.229, 0.224, 0.225])
+])
+
+# Class names (same order as in your dataset)
+class_names = ['angry', 'disgust', 'fear', 'happy', 'neutral', 'sad', 'surprise']
+
+# Function to predict the mood from the uploaded image
+def predict_mood(image):
+    image = Image.fromarray(image)
+    image = transform(image).unsqueeze(0).to(device)
+
+    with torch.no_grad():
+        outputs = model(image)
+        _, preds = torch.max(outputs, 1)
+        predicted_class = class_names[preds.item()]
+
+    return predicted_class
+
+# Gradio interface
+iface = gr.Interface(
+    fn=predict_mood,
+    inputs=gr.Image(type="numpy"),
+    outputs="text",
+    live=True,
+    title="Mood Classifier",
+    description="Upload an image of a face and the model will predict the mood."
+)
+
+# Launch the app
+iface.launch()