Update app.py

app.py

@@ -1,69 +1,19 @@
 import gradio as gr
-import torch
-import torchvision.transforms as transforms
-from PIL import Image
-from resnet import SupCEResNet  # Ensure the correct import path
+from fastapi import FastAPI
+from starlette.staticfiles import StaticFiles
 
-# ✅ Define class labels (from Clothing1M)
-class_labels = [
-    "T-Shirt", "Shirt", "Knitwear", "Chiffon", "Sweater", "Hoodie",
-    "Windbreaker", "Jacket", "Downcoat", "Suit", "Shawl", "Dress",
-    "Vest", "Underwear"
-]
+# Create a FastAPI app
+app = FastAPI()
 
-# ✅ Function to load the model
-def create_model_selfsup(net='resnet50', num_class=14, checkpoint_path='/content/ckpt_clothing_resnet50.pth'):
-    """Loads a self-supervised pretrained model for Clothing1M classification"""
-    print(f"🔄 Loading model from: {checkpoint_path}")
+# Serve the React build folder
+app.mount("/", StaticFiles(directory="build", html=True), name="static")
 
-    # Load the checkpoint safely
-    checkpoint = torch.load(checkpoint_path, map_location="cuda" if torch.cuda.is_available() else "cpu", weights_only=False)
+# Define a dummy Gradio interface (optional)
+def greet(name):
+    return f"Hello, {name}! Welcome to Flight Timings."
 
-    # Remove 'module.' prefix if using DataParallel
-    state_dict = {k.replace('module.', ''): v for k, v in checkpoint['model'].items()}
+demo = gr.Interface(fn=greet, inputs="text", outputs="text")
 
-    # Initialize and load model
-    model = SupCEResNet(net, num_classes=num_class, pool=True)
-    model.load_state_dict(state_dict, strict=False)
+# Add Gradio to FastAPI
+app = gr.mount_gradio_app(app, demo, path="/gradio")
 
-    # Move model to GPU if available
-    model = model.to("cuda" if torch.cuda.is_available() else "cpu")
-    model.eval()  # Set model to evaluation mode
-
-    print("✅ Model loaded successfully!")
-    return model
-
-# ✅ Load the model once
-model = create_model_selfsup()
-
-# ✅ Define image preprocessing function
-def preprocess_image(image):
-    """Transforms input image for the model"""
-    transform = transforms.Compose([
-        transforms.Resize(256),
-        transforms.CenterCrop(224),
-        transforms.ToTensor(),
-        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
-    ])
-    return transform(image).unsqueeze(0).to("cuda" if torch.cuda.is_available() else "cpu")
-
-# ✅ Define inference function
-def predict_clothing(image):
-    """Runs inference on an uploaded image"""
-    image = Image.fromarray(image)  # Convert numpy array to PIL Image
-    image = preprocess_image(image)  # Preprocess image
-
-    with torch.no_grad():
-        output = model(image)
-        predicted_class = torch.argmax(output, dim=1).item()  # Get class index
-
-    return class_labels[predicted_class]  # Return class name
-
-# ✅ Create Gradio Interface
-gr.Interface(
-    fn=predict_clothing,
-    inputs=gr.Image(type="numpy"),
-    outputs=gr.Textbox(label="Predicted Clothing Type"),
-    title="Clothing1M Classification",
-    description="Upload an image to classify clothing into one of 14 categories."
-).launch()