Add user interface via gradio app

.gitignore

@@ -1,3 +1,6 @@
+# Environment file
+config_api.env
+
 # Byte-compiled / optimized / DLL files
 __pycache__/
 *.py[cod]

app.py

@@ -0,0 +1,111 @@
+import os
+import requests
+import gradio as gr
+from classification.classifier import Classifier
+from dotenv import load_dotenv, find_dotenv
+import json
+
+# Initialize API URLs from env file or global settings
+def retrieve_api():
+
+    env_path = find_dotenv('config_api.env')
+    if env_path:
+        load_dotenv(dotenv_path=env_path)
+        print("config_api.env file loaded successfully.")
+    else:
+        print("config_api.env file not found.")
+
+    # Use of AWS endpoint or local container by default
+    global AWS_API
+    AWS_API = os.getenv("AWS_API", default="http://localhost:8000")
+
+def initialize_classifier():
+    global cls
+    cls = Classifier()
+
+
+def predict_class_local(sepl, sepw, petl, petw):
+    data = list(map(float, [sepl, sepw, petl, petw]))
+    results = cls.load_and_test(data)
+    return results
+
+
+def predict_class_aws(sepl, sepw, petl, petw):
+    if AWS_API == "http://localhost:8000":
+        API_endpoint = AWS_API + "/2015-03-31/functions/function/invocations"
+    else:
+        API_endpoint = AWS_API + "/test/classify"
+
+    data = list(map(float, [sepl, sepw, petl, petw]))
+    json_object = {
+        "features": [
+            data
+        ]
+    }
+
+    response = requests.post(API_endpoint, json=json_object)
+    if response.status_code == 200:
+        # Process the response
+        response_json = response.json()
+        results_dict = json.loads(response_json["body"])
+    else:
+        results_dict = {"Error": response.status_code}
+        gr.Error(f"\t API Error: {response.status_code}")
+    return results_dict
+
+
+def predict(sepl, sepw, petl, petw, type):
+    print("type: ", type)
+    if type == "Local":
+        results = predict_class_local(sepl, sepw, petl, petw)
+    elif type == "AWS API":
+        results = predict_class_aws(sepl, sepw, petl, petw)
+
+    prediction = results["predictions"][0]
+    confidence = max(results["probabilities"][0])
+
+    return f"Prediction: {prediction} \t - \t Confidence: {confidence:.3f}"
+
+
+# Define the Gradio interface
+def user_interface():
+    with gr.Blocks() as demo:
+        gr.Markdown("# IRIS classification task - use of AWS Lambda")
+        gr.Markdown(
+            """
+            Aims: Categorization of different species of iris flowers (Setosa, Versicolor, and Virginica) 
+            based on measurements of physical characteristics (sepals and petals).
+
+            Notes: This web application uses two types of predictions:
+               - local prediction (direct source code) 
+               - cloud prediction via an AWS API (i.e. use of ECR, Lambda function and API Gateway) to run the machine learning model.
+            
+            """
+        )
+
+        with gr.Row():
+            with gr.Column():
+                with gr.Group():
+                    gr_sepl = gr.Slider(minimum=4.0, maximum=8.0, step=0.1, label="Sepal Length (in cm)")
+                    gr_sepw = gr.Slider(minimum=2.0, maximum=5.0, step=0.1, label="Sepal Width (in cm)")
+                    gr_petl = gr.Slider(minimum=1.0, maximum=7.0, step=0.1, label="Petal Length (in cm)")
+                    gr_petw = gr.Slider(minimum=0.1, maximum=2.8, step=0.1, label="Petal Width (in cm)")
+            with gr.Column():
+                with gr.Row():
+                    gr_type = gr.Radio(["Local", "AWS API"], value="Local", label="Prediction type")
+                with gr.Row():
+                    gr_output = gr.Textbox(label="Prediction output")
+
+        with gr.Row():
+            submit_btn = gr.Button("Submit")
+            clear_button = gr.ClearButton()
+
+        submit_btn.click(fn=predict, inputs=[gr_sepl, gr_sepw, gr_petl, gr_petw, gr_type], outputs=[gr_output])
+        clear_button.click(lambda: None, inputs=None, outputs=[gr_output], queue=False)
+    demo.queue().launch(debug=True)
+
+
+if __name__ == "__main__":
+    retrieve_api()
+    initialize_classifier()
+    user_interface()

requirements.txt

@@ -1,7 +1,7 @@
 scikit-learn==1.6.0
 pandas==2.2.3
 numpy==2.2.0
-# streamlit==1.41.0
-# pyarrow==16.0.0
+gradio==5.5.0
 pytest==8.3.4
-joblib==1.4.2
+joblib==1.4.2
+python-dotenv==1.0.1