Upload the App.py file

app.py

@@ -0,0 +1,92 @@
+
+### 1. Imports and class names setup ###
+import gradio as gr
+import os
+import torchvision.transforms as T
+
+from model import FlowerClassificationModel
+from timeit import default_timer as timer
+from typing import Tuple, Dict
+from data_setup import classes, model_tsfm
+from utils import *
+
+# Setup class names
+#class_names = ['pizza', 'steak', 'sushi']
+
+### 2. Model and transforms preparation ###
+#test_tsfm = T.Compose([T.Resize((224,224)),
+#                        T.ToTensor(),
+#                       T.Normalize(mean=[0.485, 0.456, 0.406], # 3. A mean of [0.485, 0.456, 0.406] (across each colour channel)
+#                         std=[0.229, 0.224, 0.225]) # 4. A standard deviation of [0.229, 0.224, 0.225] (across each colour channel),
+#                       ])
+
+# Create ResNet50 Model
+flower_model = FlowerClassificationModel(num_classes=len(classes), pretrained=True)
+
+saved_path = 'flower_model_29.pth'
+
+print('Loading Model State Dictionary')
+# Load saved weights
+flower_model.load_state_dict(
+                torch.load(f=saved_path,
+                           map_location=torch.device('cpu'), # load to CPU
+                          )
+                        )
+
+print('Model Loaded ...')
+### 3. Predict function ###
+
+# Create predict function
+from typing import Tuple, Dict
+
+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 = get_image(img_path, model_tsfm).unsqueeze(0)
+    img = model_tsfm(image=np.array(img))["image"]
+    img = img.unsqueeze(0)
+
+    # Put model into evaluation mode and turn on inference mode
+    flower_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(flower_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 = {classes[i]: float(pred_probs[0][i]) for i in range(len(classes))}
+
+    # 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= 'United Kingdom Flower Classification Mini 🌻🌼🌸❀💐🌷'
+description = "An ResNet50 computer vision model to classify images of Flower Categories."
+article = "<p>Flower Classification Created by Chukwuka </p><p style='text-align: center'><a href='https://github.com/Sylvesterchuks/dogbreed_app'>Github Repo</a></p>"
+
+
+# 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=5, 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,
+                    article=article
+                   )
+# Launch the demo
+print('Gradio Demo Launched')
+demo.launch()
+