inital commit

app.py

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+
+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 = ['Real', 'Fake']
+
+### 2. Model and transforms preparation ###
+
+# Create model
+model, model_transforms = create_model(
+    num_classes=2,
+)
+
+# Load saved weights
+model.load_state_dict(
+    torch.load(
+        f="RealityCheck.pth",
+        map_location=torch.device("cpu"),  # load to CPU
+    )
+)
+
+### 3. Predict function ###
+
+# 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 = "Reality Check"
+description = "An computer vision model to classify images as Real Or Fake(Ai Generated). The Model can classify the images with 95% Accuracy"
+article = "Created at [Real Or Fake]"
+
+# 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=2, 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=2,
+                 seed:int=42):
+  weights=torchvision.models.ResNet50_Weights.DEFAULT
+  transforms=weights.transforms
+  model=torchvision.models.resnet50(weights=weights)
+
+  for param in model.parameters():
+    param.requires_grad=False
+
+  torch.manual_seed(42)
+  model.fc= torch.nn.Sequential(
+    torch.nn.Linear(2048,1000),
+    torch.nn.ReLU(),
+    torch.nn.Linear(1000,500),
+    torch.nn.Dropout(),
+    torch.nn.Linear(in_features=500,
+                    out_features=output_shape,
+                    bias=True)
+
+    )
+
+  return model,transforms
+

requirements.txt

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+torch==2.3.0
+torchvision==0.18.0
+gradio==4.32.2