Delete app.py

app.py

@@ -1,75 +0,0 @@
-### 1. Imports and class names setup ### 
-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
-
-# Setup class names
-
-
-### 2. Model and transforms preparation ###
-
-# Create EffNetB2 model
-effnetb2, effnetb2_transforms = create_effnetb2_model(
-    num_classes=102, # len(class_names) would also work
-)
-
-# Load saved weights
-effnetb2.load_state_dict(
-    torch.load(
-        f="pretrained_effnetb2_feature_extractor_fl102.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 = effnetb2_transforms(img).unsqueeze(0)
-    
-    # Put model into evaluation mode and turn on inference mode
-    effnetb2.eval()
-    with torch.inference_mode():
-        # Pass the transformed image through the model and turn the prediction logits into prediction probabilities
-        pred_probs = torch.softmax(effnetb2(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 = "Flofi Mini"
-description = "An EfficientNetB2 feature extractor computer vision model to classify images of 102 flower species."
-article = "Created by Haydar Uçar."
-
-# Create examples list from "examples/" directory
-
-
-# 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 
-                    title=title,
-                    description=description,
-                    article=article)
-
-# Launch the demo!
-demo.launch()