push first instance

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_model
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+from torchvision import transforms
+
+# Setup class names
+class_names = ["Happy", "Sad", "Disgusted","Suprised","Fearful","Angry","Neutral"]
+
+### 2. Model and transforms preparation ###
+
+# Create EffNetB2 model
+effnetb0, effnetb0_transforms = create_effnetb0_model(
+    num_classes=7, # len(class_names) would also work
+)
+
+# Load saved weights
+effnetb0.load_state_dict(
+    torch.load(
+        f="models/efficientnet_b0.pth",
+        map_location=torch.device("cpu"),  # load to CPU
+    )
+)
+
+### 3. Predict function ###
+
+# Create predict function
+
+def predict(inp):
+  inp = transforms.ToTensor()(inp).unsqueeze(0)
+  with torch.no_grad():
+    prediction = torch.nn.functional.softmax(effnetb0(inp)[0], dim=0)
+    confidences = {class_names[i]: float(prediction[i]) for i in range(len(class_names))}    
+  return confidences
+
+example_list = [["examples/" + example] for example in os.listdir("examples")]
+
+### 4. Gradio app ###
+
+# Create title, description and article strings
+title = "Emotion Detection App 😀😐😰😞🤢😲😡"
+description = "An EfficientNetB0 computer vision model to classify images of emotions: Happy, Neutral, Sad, fearful, Angry, Suprised, Disgusted."
+article = "Reference: [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
+
+import gradio as gr
+
+gr.Interface(fn=predict, 
+             inputs=gr.Image(type="pil"),
+             outputs=gr.Label(num_top_classes=7),
+             examples=example_list,
+                             title=title,
+                    description=description,
+                    article=article).launch()

model.py

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+import torch
+import torchvision
+
+from torch import nn
+import torchvision.transforms 
+
+def create_effnetb0_model(num_classes:int=7, 
+                          seed:int=42):
+    """Creates an EfficientNetB2 feature extractor model and transforms.
+
+    Args:
+        num_classes (int, optional): number of classes in the classifier head. 
+            Defaults to 3.
+        seed (int, optional): random seed value. Defaults to 42.
+
+    Returns:
+        model (torch.nn.Module): EffNetB0 feature extractor model. 
+        transforms (torchvision.transforms): EffNetB0 image transforms.
+    """
+    # Create EffNetB0 pretrained weights, transforms and model
+    weights = torchvision.models.EfficientNet_B0_Weights.DEFAULT
+    transforms = weights.transforms()
+    model = torchvision.models.efficientnet_b0(weights=weights)
+
+    # Freeze all layers in base model
+    for param in model.parameters():
+        param.requires_grad = False
+
+    # Change classifier head with random seed for reproducibility
+    torch.manual_seed(seed)
+    model.classifier = nn.Sequential(
+        nn.Dropout(p=0.3, inplace=True),
+        nn.Linear(in_features=1280, out_features=num_classes),
+    )
+    
+    return model, transforms

models/efficientnet_b0.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1892c1219503678b193e8f0f0f8d2beff96bd995c749418ccb9a7f8e76dee102
+size 16361111

requirements.txt

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+<<<<<<< HEAD
+torch==1.13.0
+torchvision==0.14.0
+gradio==3.1.4
+=======
+version https://git-lfs.github.com/spec/v1
+oid sha256:9ebc76c7c29ea13254e28d4372eaa9191f593f4bca0c04cbb56416b3c5dfadd7
+size 48
+>>>>>>> 5d0db446 (added app and model)