adding necessary files -kai

app.py

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+### 1. Imports and class names setup ###
+import gradio as gr
+import os
+import torch
+
+from model import create_effnetb0
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+# Setup class names
+class_names = ['cup_cakes', 'donuts', 'french_fries', 'ice_cream']
+
+### 2. Model and transforms preparation ###
+
+# Create EffNetB0 model
+effnetb0, effnetb0_transforms = create_effnetb0()
+
+# Load saved weights
+effnetb0.load_state_dict(
+    torch.load(
+        f="effnetb0_data20_10epoch.pth",
+        map_location=torch.device("cpu"),  # load to CPU
+    )
+)
+
+
+### 3. Predict function ###
+
+# Create predict function
+def predict(img) -> Tuple[Dict, float]:
+    """Transforms and performs a prediction on img and returns prediction and time taken.
+    """
+    # Start the timer
+    start_time = timer()
+
+    # Transform the target image and add a batch dimension
+    img = effnetb0_transforms(img).unsqueeze(0)
+
+    # Put model into evaluation mode and turn on inference mode
+    effnetb0.eval()
+    with torch.inference_mode():
+        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
+        pred_probs = torch.softmax(effnetb0(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
+
+
+### 4. Gradio app ###
+
+# Create title, description and article strings
+title = "Food Classifier"
+description = "An EfficientNetB0 feature extractor computer vision model to classify images of French_fries, Cup_cakes, Ice_Cream, and Donuts."
+article = "Created at google collab. Documentation at https://medium.com/me/stories/public, Code repository at https://github.com/Alyxx-The-Sniper/CNN "
+
+# Create examples list from "examples/" directory
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+# Create the Gradio demo
+demo = gr.Interface(fn=predict,  # mapping function from input to output
+                    inputs=gr.Image(type="pil"),  # what are the inputs?
+                    outputs=[gr.Label(num_top_classes=4, label="Predictions"),  # what are the outputs?
+                             gr.Number(label="Prediction time (s)")],
+                    # our fn has two outputs, therefore we have two outputs
+                    # Create examples list from "examples/" directory
+                    examples=example_list,
+                    title=title,
+                    description=description,
+                    article=article)
+
+# Launch the demo!
+demo.launch()
+

effnetb0_data20_10epoch.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:572112ada0d654f7b606531764fb846badcb322f690b4343a92720b3d68daa3f
+size 16354321

model.py

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+import torchvision
+from torch import nn
+
+def create_effnetb0(num_classes:int=4, seed:int=42):
+    # 1. Get the base mdoel with pretrained weights and send to target device
+    weights = torchvision.models.EfficientNet_B0_Weights.DEFAULT
+    transforms = weights.transforms()
+    model = torchvision.models.efficientnet_b0(weights=weights)#.to(device)
+
+    # 2. Freeze the base model layers
+    for param in model.features.parameters():
+        param.requires_grad = False
+
+    # 3. Change the classifier head
+    model.classifier = nn.Sequential(
+        nn.Dropout(p=0.2),
+        nn.Linear(in_features=1280, out_features=num_classes)
+    )#.to(device)
+
+    # 5. Give the model a name
+    model.name = "effnetb0"
+    print(f"[INFO] Created new {model.name} model.")
+    return model, transforms

requirements.txt

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+torch==1.12.0
+torchvision==0.13.0
+gradio==3.1.4