[Task] Model Deployment

[Description] Initial Deployment of the model.
[Author]

@alifalhasan

app.py

@@ -0,0 +1,13 @@
+import gradio as gr
+from src.classify.classify import classify_logo
+
+if __name__ == "__main__":
+    # Create the Gradio interface
+    iface = gr.Interface(
+        fn=classify_logo,
+        inputs=[gr.Image(type="filepath")],
+        outputs=gr.Textbox(label="Predicted class")
+    )
+
+    # Launch the interface
+    iface.launch(share=True)

flagged/log.csv

@@ -0,0 +1,8 @@
+image_file,output,flag,username,timestamp
+"{""path"":""flagged\\image_file\\0241897a7b97af036402\\arsenal.png"",""url"":""http://127.0.0.1:7860/file=C:\\Users\\HP\\AppData\\Local\\Temp\\gradio\\6de795593f21b8d7a13da285587d95c0f2cb56e1\\arsenal.png"",""size"":213946,""orig_name"":""arsenal.png"",""mime_type"":""""}","{""label"":null,""confidences"":null}",,,2024-01-03 13:51:51.930046
+"{""path"":""flagged\\image_file\\22e0c55976b127358232\\arsenal.png"",""url"":""http://127.0.0.1:7860/file=C:\\Users\\HP\\AppData\\Local\\Temp\\gradio\\6de795593f21b8d7a13da285587d95c0f2cb56e1\\arsenal.png"",""size"":213946,""orig_name"":""arsenal.png"",""mime_type"":""""}","{""label"":null,""confidences"":null}",,,2024-01-03 13:51:53.897584
+"{""path"":""flagged\\image_file\\6e1a7134efc60a0f7f84\\arsenal.png"",""url"":""http://127.0.0.1:7860/file=C:\\Users\\HP\\AppData\\Local\\Temp\\gradio\\90eac5362cf4c9612ee37f3436f2612954cc6327\\arsenal.png"",""size"":41502,""orig_name"":""arsenal.png"",""mime_type"":""""}","{""label"":null,""confidences"":null}",,,2024-01-03 14:04:38.780049
+"{""path"":""flagged\\image_file\\392154eff274987c347e\\arsenal.png"",""url"":""http://127.0.0.1:7860/file=C:\\Users\\HP\\AppData\\Local\\Temp\\gradio\\90eac5362cf4c9612ee37f3436f2612954cc6327\\arsenal.png"",""size"":41502,""orig_name"":""arsenal.png"",""mime_type"":""""}","{""label"":null,""confidences"":null}",,,2024-01-03 14:04:40.283244
+"{""path"":""flagged\\image_file\\11b334fd25d35a4399a3\\arsenal.png"",""url"":""http://127.0.0.1:7860/file=C:\\Users\\HP\\AppData\\Local\\Temp\\gradio\\90eac5362cf4c9612ee37f3436f2612954cc6327\\arsenal.png"",""size"":41502,""orig_name"":""arsenal.png"",""mime_type"":""""}","{""label"":null,""confidences"":null}",,,2024-01-03 14:04:41.448505
+"{""path"":""flagged\\image_file\\e738d68c7eea5642416a\\arsenal.png"",""url"":""http://127.0.0.1:7860/file=C:\\Users\\HP\\AppData\\Local\\Temp\\gradio\\90eac5362cf4c9612ee37f3436f2612954cc6327\\arsenal.png"",""size"":41502,""orig_name"":""arsenal.png"",""mime_type"":""""}","{""label"":null,""confidences"":null}",,,2024-01-03 14:04:42.925141
+"{""path"":""flagged\\image_file\\46b5bc411c2037dec67a\\arsenal.png"",""url"":""http://127.0.0.1:7860/file=C:\\Users\\HP\\AppData\\Local\\Temp\\gradio\\90eac5362cf4c9612ee37f3436f2612954cc6327\\arsenal.png"",""size"":41502,""orig_name"":""arsenal.png"",""mime_type"":""""}","{""label"":null,""confidences"":null}",,,2024-01-03 14:09:47.459632

src/classify/classify.py

@@ -0,0 +1,54 @@
+"""classify.py
+
+This module classifies the input image.
+
+"""
+
+import numpy as np
+import tensorflow as tf
+from tensorflow.keras.preprocessing import image
+
+# Load the trained model
+import os
+
+script_directory = os.path.dirname(os.path.abspath(__file__))
+model_path = os.path.join(script_directory, '../../models/football_logo_model.h5')
+
+model = tf.keras.models.load_model(model_path)
+
+def preprocess_image(img):
+    """
+    Preprocess the input image for model prediction.
+
+    Args:
+        img: Input image.
+
+    Returns:
+        img: Preprocessed image.
+    """
+    img = image.img_to_array(img)
+    img = np.expand_dims(img, axis=0)
+    img /= 255.0
+    return img
+
+# Define the class names (replace with your actual class names)
+class_names = ['Arsenal', 'Chelsea', 'Liverpool', 'Manchester City', 'Manchester United']
+
+def classify_logo(img):
+    """
+    Classify the football logo in the input image.
+
+    Args:
+        img: Path to the input image.
+
+    Returns:
+        str: The predicted class of the football logo.
+    """
+    img_path = img
+    img = image.load_img(img_path, target_size=(224, 224))
+    img = image.img_to_array(img)
+    img = preprocess_image(img)
+    prediction = model.predict(img)
+    predicted_class_index = prediction.argmax(axis=1)[0]
+    predicted_class_name = class_names[predicted_class_index]  # Map index to name
+    return predicted_class_name