first commit

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* 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
+0pr_food.pth filter=lfs diff=lfs merge=lfs -text

0pr_food.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1dda579ec9a9442254372397d99d56a350d047ceead84386afad9a8436728b7f
+size 31296826

app.py

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+import gradio as gr
+import os
+import torch
+from model import create_effnetb2_model
+from timeit import default_timer as timer
+from typing import Tuple,Dict
+
+with open("class_names.txt","r") as f:
+    class_names = [food_name.strip() for food_name in f.readlines()]
+effnetb2,effnetb2_transforms=create_effnetb2(classes=7)
+effnetb2.load_state_dict(torch.load(f="0pr_food.pth",map_location=torch.device("cpu")))
+def predict(img)-> Tuple[Dict,float]:
+    start_timer=timer()
+    img=effnetb2_transforms(img).unsqueeze(0)
+    effnetb2.eval()
+    with torch.inference_mode():
+        pred_probs=torch.softmax(effnetb2(img),dim=1)
+    pred_labels_and_probs={class_names[i]:float(pred_probs[0][i] for i in range(len(class_names)))}
+    pred_time=round(timer()-start_timer,5)
+    return pred_labels_and_probs,pred_time
+title="FoodVision"
+description="An EfficientNetB2 feature extractor computer vision model to classify images of & food [samosa,pizza,steak,sushi,cup cakes,french fries,omelette]"
+article="Created at [Foodvision.ipynb]"
+example_list=[["Ex/"+ example] for example in os.listdir("Ex")]
+demo=gr.Interface(fn=predict,inputs=gr.Image(type="pil"),outputs=[gr.Label(num_top_classes=2,label="Preditions"),gr.Number(label="Predcition time(s)")],examples=example_list,title=title,description=description,article=article)
+demo.launch()

class_names.txt

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+cup_cakes
+french_fries
+omelette
+pizza
+samosa
+steak
+sushi

model.py

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+import torch
+import torchvision
+from torch import nn
+
+def create_effnetb2(classes:int=3,seed:int=42):
+    weights=torchvision.models.EfficientNet_B2_Weights.DEFAULT
+    transforms=weights.transforms()
+    model=torchvision.models.efficientnet_b2(weights=weights)
+    for p in model.parameters():
+        p.requires_grad=False
+    torch.manual_seed(seed)
+    model.classifier=nn.Sequential(nn.Dropout(p=0.3,inplace=True),nn.Linear(1408,classes))
+    return model,transforms

requirements.txt

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+torch==2.5.1
+torchvision==0.20.1
+gradio==5.29.1