first deploy

README.md

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----
-title: FoodVision Mini
-emoji: 👁
-colorFrom: purple
-colorTo: yellow
-sdk: gradio
-sdk_version: 3.50.2
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+### 1. Imports and class names setup ### 
+import gradio as gr
+import os
+import torch
+
+from model import create_model
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+# Setup class names
+class_names = ["pizza", "steak", "sushi"]
+
+### 2. Model and transforms preparation ###
+
+# Create model
+model, model_transforms = create_model(
+    num_classes=3, # len(class_names) would also work
+)
+
+# Load saved weights
+model.load_state_dict(
+    torch.load(
+        f="pretrained_model.pth",
+        map_location=torch.device("cpu"),  # load to CPU
+    )
+)
+
+# 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 = model_transforms(img).unsqueeze(0)
+    
+    # Put model into evaluation mode and turn on inference mode
+    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(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
+
+### 4. Gradio app ###
+
+# Create title, description and article strings
+title = "FoodVision Mini 🍕🥩🍣"
+description = "An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi."
+article = "Created at [09. PyTorch Model Deployment](https://www.learnpytorch.io/09_pytorch_model_deployment/)."
+
+# 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=3, 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()

model.py

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+import torch
+import torchvision
+
+from torch import nn
+
+
+def create_model(num_classes:int=3, 
+                        seed:int=42):
+    """Creates the 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): EffNetB2 feature extractor model. 
+        transforms (torchvision.transforms): EffNetB2 image transforms.
+    """
+    # Create EffNetB2 pretrained weights, transforms and model
+    weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
+    transforms = weights.transforms()
+    model = torchvision.models.efficientnet_b2(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=1408, out_features=num_classes),
+    )
+    
+    return model, transforms

pretrained_model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2e6bf003d5bc481ed6499462f7158237bd74ad534bcca7cdefb7ad403345cdf6
+size 31307450

requirements.txt

@@ -0,0 +1,204 @@
+aiofiles==23.2.1
+    # via gradio
+altair==5.1.2
+    # via gradio
+annotated-types==0.6.0
+    # via pydantic
+anyio==3.7.1
+    # via
+    #   fastapi
+    #   httpcore
+    #   starlette
+attrs==23.1.0
+    # via
+    #   jsonschema
+    #   referencing
+certifi==2023.7.22
+    # via
+    #   httpcore
+    #   httpx
+    #   requests
+charset-normalizer==3.3.1
+    # via requests
+click==8.1.7
+    # via uvicorn
+colorama==0.4.6
+    # via
+    #   click
+    #   tqdm
+contourpy==1.1.1
+    # via matplotlib
+cycler==0.12.1
+    # via matplotlib
+fastapi==0.104.0
+    # via gradio
+ffmpy==0.3.1
+    # via gradio
+filelock==3.12.4
+    # via
+    #   huggingface-hub
+    #   torch
+fonttools==4.43.1
+    # via matplotlib
+fsspec==2023.10.0
+    # via
+    #   gradio-client
+    #   huggingface-hub
+    #   torch
+gradio==3.50.2
+    # via -r requirements.in
+gradio-client==0.6.1
+    # via gradio
+h11==0.14.0
+    # via
+    #   httpcore
+    #   uvicorn
+httpcore==0.18.0
+    # via httpx
+httpx==0.25.0
+    # via
+    #   gradio
+    #   gradio-client
+huggingface-hub==0.18.0
+    # via
+    #   gradio
+    #   gradio-client
+idna==3.4
+    # via
+    #   anyio
+    #   httpx
+    #   requests
+importlib-resources==6.1.0
+    # via gradio
+jinja2==3.1.2
+    # via
+    #   altair
+    #   gradio
+    #   torch
+jsonschema==4.19.1
+    # via altair
+jsonschema-specifications==2023.7.1
+    # via jsonschema
+kiwisolver==1.4.5
+    # via matplotlib
+markupsafe==2.1.3
+    # via
+    #   gradio
+    #   jinja2
+matplotlib==3.8.0
+    # via gradio
+mpmath==1.3.0
+    # via sympy
+networkx==3.2
+    # via torch
+numpy==1.26.1
+    # via
+    #   altair
+    #   contourpy
+    #   gradio
+    #   matplotlib
+    #   pandas
+    #   torchvision
+orjson==3.9.9
+    # via gradio
+packaging==23.2
+    # via
+    #   altair
+    #   gradio
+    #   gradio-client
+    #   huggingface-hub
+    #   matplotlib
+pandas==2.1.1
+    # via
+    #   altair
+    #   gradio
+pillow==10.1.0
+    # via
+    #   gradio
+    #   matplotlib
+    #   torchvision
+pydantic==2.4.2
+    # via
+    #   fastapi
+    #   gradio
+    #   pydantic-settings
+pydantic-core==2.10.1
+    # via pydantic
+pydantic-settings==2.0.3
+    # via -r requirements.in
+pydub==0.25.1
+    # via gradio
+pyparsing==3.1.1
+    # via matplotlib
+python-dateutil==2.8.2
+    # via
+    #   matplotlib
+    #   pandas
+python-dotenv==1.0.0
+    # via
+    #   -r requirements.in
+    #   pydantic-settings
+python-multipart==0.0.6
+    # via gradio
+pytz==2023.3.post1
+    # via pandas
+pyyaml==6.0.1
+    # via
+    #   gradio
+    #   huggingface-hub
+referencing==0.30.2
+    # via
+    #   jsonschema
+    #   jsonschema-specifications
+requests==2.31.0
+    # via
+    #   gradio
+    #   gradio-client
+    #   huggingface-hub
+    #   torchvision
+rpds-py==0.10.6
+    # via
+    #   jsonschema
+    #   referencing
+semantic-version==2.10.0
+    # via gradio
+six==1.16.0
+    # via python-dateutil
+sniffio==1.3.0
+    # via
+    #   anyio
+    #   httpcore
+    #   httpx
+starlette==0.27.0
+    # via fastapi
+sympy==1.12
+    # via torch
+toolz==0.12.0
+    # via altair
+torch==2.1.0
+    # via
+    #   -r requirements.in
+    #   torchvision
+torchvision==0.16.0
+    # via -r requirements.in
+tqdm==4.66.1
+    # via huggingface-hub
+typing-extensions==4.8.0
+    # via
+    #   fastapi
+    #   gradio
+    #   gradio-client
+    #   huggingface-hub
+    #   pydantic
+    #   pydantic-core
+    #   torch
+tzdata==2023.3
+    # via pandas
+urllib3==2.0.7
+    # via requests
+uvicorn==0.23.2
+    # via gradio
+websockets==11.0.3
+    # via
+    #   gradio
+    #   gradio-client