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20_percent_data_effnet1.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:df47133107a62e2ebf218c2067cd825f02a071e5b22cd08b8f226ab63e9d8e3d
+size 26508218

app.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Thu Feb  8 13:00:08 2024
+
+@author: firis
+"""
+
+import gradio as gr
+import os
+import torch
+
+from model import create_eff_model
+from timeit import default_timer as timer
+
+class_names=["pizza","steak","sushi"]
+
+
+eff_model,eff_model_transform=create_eff_model() #bu standart model
+eff_model_dict=torch.load("20_percent_data_effnet1.pth")
+eff_model.load_state_dict(eff_model_dict)
+eff_model.to("cpu")
+
+#prediction function
+
+def predict(img):
+   
+    start_time = timer()
+    img=eff_model_transform(img).unsqueeze(0)
+    
+    eff_model.eval()
+    with torch.inference_mode():
+        pred_and_probs=torch.softmax(eff_model(img),dim=1)
+        
+    class_with_pred_dict={cl:float(pred_and_probs[0][ind]) for ind,cl in enumerate(class_names)}
+    
+    pred_time = round(timer() - start_time, 5)
+    
+    return class_with_pred_dict, pred_time
+
+
+############# Gradio Interface ##########
+title = "FoodVision Mini 🍕🥩🍣"
+description = "An EfficientNetB2 feature extractor computer vision model to classify images of food as pizza, steak or sushi."
+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
+                    examples=example_list, 
+                    title=title,
+                    description=description)
+
+# Launch the demo!
+demo.launch(debug=False, # print errors locally?
+            share=True)
+
+    
+    
+    
+
+
+

model.py

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+# -*- coding: utf-8 -*-
+"""
+Created on Thu Feb  8 13:48:19 2024
+
+@author: firis
+"""
+
+import torch
+import torchvision
+from torch import nn
+
+def create_eff_model(num_classes:int=3,seed:int=42):
+    
+    weights=torchvision.models.EfficientNet_B1_Weights.DEFAULT
+    transforms=weights.transforms()
+    model =torchvision.models.efficientnet_b1(weights=weights)
+
+    # Freeze all layers in base model
+    for param in model.parameters():
+        param.requires_grad = False
+        
+    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
+          
+    
+
+     

requirements.txt.txt

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+There are three requirements only:
+1. torch==1.12.0
+2. torchvision==0.13.0
+3. gradio==3.1.4