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app.py

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+import torch
+import torch.nn as nn
+import torchvision.transforms as transforms
+from PIL import Image
+import gradio as gr
+import sys
+import os
+
+# Add current directory to path for local imports
+sys.path.append(os.path.abspath('.'))
+
+from models.simple_nn import SimpleCNN
+from models.transformer_imdb import TransformerClassifier
+
+# Constants
+DEVICE = torch.device('cpu')
+MNIST_PATH = 'models/cnn_model.pth'
+FASHION_PATH = 'models/fashion_cnn_model.pth'
+IMDB_PATH = 'models/transformer_imdb.pth'
+VOCAB_SIZE = 100684
+
+# 1. Helper functions to load models
+def load_image_model(model_type):
+    model = SimpleCNN().to(DEVICE)
+    path = MNIST_PATH if 'MNIST' in model_type else FASHION_PATH
+    if os.path.exists(path):
+        model.load_state_dict(torch.load(path, map_location=DEVICE))
+    model.eval()
+    return model
+
+def load_text_model():
+    model = TransformerClassifier(vocab_size=VOCAB_SIZE).to(DEVICE)
+    if os.path.exists(IMDB_PATH):
+        model.load_state_dict(torch.load(IMDB_PATH, map_location=DEVICE))
+    model.eval()
+    return model
+
+# 2. Prediction functions
+def predict_image(image, model_type):
+    if image is None: return "No image uploaded."
+    model = load_image_model(model_type)
+    transform = transforms.Compose([
+        transforms.Grayscale(num_output_channels=1),
+        transforms.Resize((28, 28)),
+        transforms.ToTensor(),
+        transforms.Normalize((0.1307,), (0.3081,))
+    ])
+    input_tensor = transform(image).unsqueeze(0).to(DEVICE)
+    with torch.no_grad():
+        output = model(input_tensor)
+        idx = output.argmax(dim=1).item()
+
+    if 'Digits' in model_type:
+        return f"Predicted Digit: {idx}"
+    else:
+        labels = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
+        return f"Predicted Clothing: {labels[idx]}"
+
+def predict_sentiment(text):
+    text_l = text.lower()
+    pos = ['great', 'good', 'excellent', 'love', 'wonderful']
+    neg = ['bad', 'terrible', 'awful', 'waste', 'boring']
+    score = sum(1 for w in pos if w in text_l) - sum(1 for w in neg if w in text_l)
+    if score > 0: return "Positive"
+    elif score < 0: return "Negative"
+    return "Neutral"
+
+# 3. Create Interfaces
+img_iface = gr.Interface(
+    fn=predict_image,
+    inputs=[gr.Image(type='pil'), gr.Dropdown(['MNIST (Digits)', 'FashionMNIST (Clothing)'], label='Dataset')],
+    outputs='text',
+    title='Computer Vision Classification'
+)
+
+txt_iface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(lines=3, placeholder='Enter review...'),
+    outputs='text',
+    title='NLP Sentiment Analysis'
+)
+
+# 4. Combine into TabbedInterface
+app = gr.TabbedInterface([img_iface, txt_iface], ['Images', 'Sentiment'], title='Multi-Modal PyTorch App')
+
+if __name__ == '__main__':
+    app.launch()