Upload 9 files

11-model_deployment_effnetb2.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d6ecd08208539742ad16732887698bed25ce873a0981c11d01aedd641c9f0ecc
+size 31285489

FoodVision_Mini/.gitattributes

@@ -0,0 +1,35 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

FoodVision_Mini/README.md

@@ -0,0 +1,14 @@
+---
+title: FoodVision Mini
+emoji: 🐠
+colorFrom: pink
+colorTo: purple
+sdk: gradio
+sdk_version: 5.49.1
+app_file: app.py
+pinned: false
+license: mit
+short_description: classify pizza,  steak and sushi
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,54 @@
+### 1 Imports and class names setup###
+import gradio as gr
+import os 
+import torch
+from model import create_effnetb2_model
+from timeit import default_timer as timer
+from typing import List, Dict
+
+class_names = ["pizza", "steak", "sushi"]
+
+### 2 model and transform preparation###
+effnetb2, transforms = create_effnetb2_model(num_classes=len(class_names))
+effnetb2_loaded.load_state_dict("11-model_deployment_effnetb2.pth",map_location="cpu")
+
+
+### 3 we need a predict function###
+def predict(img) -> Tuple[Dict,float]:
+    #start a timer
+    start_time = timer()
+
+    # transform the image
+    transformed_image = effnet_transform(img).unsqueeze(0)
+    
+    # putting the model in eval mode and make the prediction  
+    effnetb2_loaded.eval()
+    with torch.inference_mode():
+        logit = effnetb2_loaded(transformed_image)
+
+        probs = torch.softmax(logit, dim=1)
+        # Create a prediction label and prediction probability dictionary
+        pred_label_dict ={class_names[i] : probs[0][i].item() for i in range(len(class_names))}
+
+    # calculate the pred time 
+    end_time = timer()
+    inference_time = round(end_time - start_time, 4)
+    # return the label dict and inference time
+    return pred_label_dict, inference_time
+###Grad###
+title = "FoodVision mini models 🍕,🥩,🍣"
+description = "An EfficientnetB2 feature extraction model is used to classifay images as pizza, steak, sushi"
+
+example_list =[["example/"+example] for example in os.listdir("example")]
+# create a gradio demo
+demo = gr.Interface(
+    fn=predict,
+    inputs=gr.Image(type="pil"),
+    outputs=[gr.Label(num_top_classes = 3,label= "prediction"),
+             gr.Number(label=" Prediction time in second")],
+    examples=example_list[0],
+    title=title,
+    description=description
+)
+demo.launch(share= False)
+

model.py

@@ -0,0 +1,36 @@
+import torch
+import torchvision
+
+from torch import nn
+
+
+def create_effnetb2_model(num_classes:int=3, 
+                          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): 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

requirements.txt

@@ -0,0 +1,3 @@
+torch ==2.7.1+cu118
+torchvision ==0.8.2+cu118
+gradio ==5.49.0