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
+effnet_b2.pt filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,53 @@
+import gradio as gr
+import torch
+import os
+from model import create_effnet_b2
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+
+class_names = ["pizza", "steak", "sushi"]
+
+effbet_b2_model , efftnet_b2_transform = create_effnet_b2()
+
+effbet_b2_model.load_state_dict(torch.load(f = "./effnet_b2.pt", map_location = torch.device("cpu")))
+
+def predict(img)-> Tuple[Dict,float]:
+    
+    start_time = timer()
+    img = efftnet_b2_transform(img).unsqueeze(0)
+    
+    effbet_b2_model.eval()
+    
+    with torch.inference_mode():
+        pred_prob = torch.softmax(effbet_b2_model(img), 1)
+        
+    pred_label_probs = {class_names[i] : float(pred_prob[0][i]) for i in range(len(class_names))}
+    
+    end_time = timer()
+    pred_time = round(end_time - start_time , 4)
+    
+    return pred_label_probs, pred_time
+
+
+import os
+# Create separate output components
+exmaple_list = [["examples/" + example] for example in os.listdir("examples")]
+label_output = gr.Label(label="Classification Probabilities")
+number_output = gr.Number(label="Inference Time (seconds)")  # Changed label to be more accurate
+
+demo = gr.Interface(
+    fn=predict, 
+    inputs="image", 
+    outputs=[label_output, number_output],
+    examples=exmaple_list,  # Handle case where image_path might be None
+    title="Food Vision Mini 🍕",
+    description="Upload an image to see classification probabilities and inference time.Finetuned on effnet_b2 on(pizza,steak,sushi)",
+    article="Created By sachin",
+    allow_flagging="never"
+)
+
+demo.launch(share=True, debug=True)
+    
+
+    
+

effnet_b2.pt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7e11a4d8c93f583b364335ba2b333b819489a2a583df792e03abc377ab1ef963
+size 31267706

model.py

@@ -0,0 +1,24 @@
+
+from torchvision.models import EfficientNet_B2_Weights, efficientnet_b2
+from torch import nn
+
+def  create_effnet_b2(num_classes:int = 3, seed:int = 42):
+
+    eff_weights = EfficientNet_B2_Weights.DEFAULT
+    efficientnet_transform = eff_weights.transforms()
+
+    effnet_model = efficientnet_b2(eff_weights)
+    
+    
+    for params in effnet_model.parameters():
+        params.requires_grad = False
+        
+    torch.manual_seed(seed=seed)
+     
+    effnet_model.classifier = nn.Sequential(
+            nn.Dropout(p=0.3, inplace=True),
+            nn.Linear(in_features=1408, out_features=num_classes, bias=True)
+    )   
+    
+        
+    return effnet_model, efficientnet_transform

requirements.txt

@@ -0,0 +1,4 @@
+torch==2.6.0
+torchvision==0.21.0
+gradio==5.16.0
+