Update app.py

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import gradio as gr
import os
import torch

from model import create_ViT
from timeit import default_timer as timer
from typing import Tuple, Dict

# Setup class names
with open("class_names.txt", "r") as f:
class_names = [food_name.strip() for food_name in f.readlines()]


# Create model
ViT_model, ViT_transforms = create_ViT(
num_classes=126,
)

# Load saved weights
ViT_model.load_state_dict(
torch.load(
f="ViT.pth",
map_location=torch.device("cpu"),
)
)


# Create predict function
def predict(img) -> Tuple[Dict, float]:

start_time = timer()

# Transform the target image and add a batch dimension
img = ViT_transforms(img).unsqueeze(0)

# Put model into evaluation mode and turn on inference mode
ViT_model.eval()
with torch.inference_mode():
# Pass the transformed image through the model and turn the prediction logits into prediction probabilities
pred_probs = torch.softmax(ViT_model(img), dim=1)

# Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}

# Calculate the prediction time
pred_time = round(timer() - start_time, 5)

# Return the prediction dictionary and prediction time
return pred_labels_and_probs, pred_time


##GRADIO APP
# Create title, description and article strings
title = "FoodVision🍔🍟🍦"
description = "A Vision Transformer feature extractor computer vision model to classify images of food into 126 different classes."
article = "Created by [Rohit](https://github.com/ItsNotRohit02)."

# Create examples list from "examples/" directory
example_list = [["examples/" + example] for example in os.listdir("examples")]

# Create Gradio interface
demo = gr.Interface(
fn=predict,
inputs=gr.Image(type="pil"),
outputs=[
gr.Label(num_top_classes=5, label="Predictions"),
gr.Number(label="Prediction time (s)"),
],
examples=example_list,
title=title,
description=description,
article=article,
)

# Launch the app!
demo.launch()

app.py

@@ -12,40 +12,37 @@ with open("class_names.txt", "r") as f:
 
 
 # Create model
-ViT_model, ViT_transforms = create_ViT(
-    num_classes=126,
-)
+model = create_ViT()
 
 # Load saved weights
-ViT_model.load_state_dict(
+model.load_state_dict(
     torch.load(
-        f="ViT.pth",
+        f="ViTHg.pth",
         map_location=torch.device("cpu"),
     )
 )
 
 
-# Create predict function
 def predict(img) -> Tuple[Dict, float]:
 
     start_time = timer()
 
-    # Transform the target image and add a batch dimension
-    img = ViT_transforms(img).unsqueeze(0)
+    preprocess = transforms.Compose([
+        transforms.Resize((224, 224)),
+        transforms.ToTensor(),
+        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    ])
+
+    img = preprocess(img).unsqueeze(0)  # Add batch dimension
 
-    # Put model into evaluation mode and turn on inference mode
-    ViT_model.eval()
+    model.eval()
     with torch.inference_mode():
-        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
-        pred_probs = torch.softmax(ViT_model(img), dim=1)
+        pred_probs = torch.softmax(model(img), dim=1)
 
-    # Create a prediction label and prediction probability dictionary for each prediction class (this is the required format for Gradio's output parameter)
     pred_labels_and_probs = {class_names[i]: float(pred_probs[0][i]) for i in range(len(class_names))}
 
-    # Calculate the prediction time
     pred_time = round(timer() - start_time, 5)
 
-    # Return the prediction dictionary and prediction time
     return pred_labels_and_probs, pred_time