Delete app.py

app.py

@@ -1,75 +0,0 @@
-### 1. Imports and class names setup ###
-import gradio as gr
-import os
-import torch
-
-from class_names import class_names
-from model import Load_model
-from timeit import default_timer as timer
-from typing import Tuple, Dict
-
-
-### 1. Model and transforms preparation ###
-
-# Create model and transform
-model, transforms = Load_model()
-
-# Load saved weights
-def load_checkpoint(checkpoint_file, model, device='cpu'):
-    print("=> Loading checkpoint")
-    checkpoint = torch.load(checkpoint_file, map_location=device)
-    model.load_state_dict(checkpoint["state_dict"])
-load_checkpoint('model_checkpoint.pt', model)
-
-### 2. 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 = 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
-
-
-### 3. Gradio app ###
-
-# Create title, description and article strings
-title = "BirdVision 500 🦅🦆🐦🕊🦤🦢🦜"
-description = "A model based on YoLov8 classification 500 birds."
-article = "Created on [GITHUB](https://github.com/vvduc1803/Yolov8_cls)."
-
-# 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=10, 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()