Add user feedbacks and improve message

app.py

@@ -1,10 +1,20 @@
-from turtle import title
 import gradio as gr
 from transformers import pipeline
 from PIL import Image
+import os
+from huggingface_hub import HfApi, upload_file
+import io
+import numpy as np
+import uuid
 
 # Initialize the pipeline with your model
 pipe = pipeline("image-classification", model="SubterraAI/ofwat_defects_classification")
+HF_TOKEN = os.getenv('HF_TOKEN')
+DATASET_NAME = "SubterraAI/ofwat_defects_loop"
+hf_api = HfApi()
+
+# Directory where the flagged images will be saved
+flagged_data_dir = "./flagged_data"
 
 defect_full_names_list = [
     "Crack Longitudinal",
@@ -136,6 +146,32 @@ defect_dict = {
     "CU": "Camera Underwater"
 }
 
+def simple_flag(image, label):
+    # Convert the input image to PIL format and save to a BytesIO object
+    pil_image = Image.fromarray(image.astype(np.uint8))
+    img_byte_arr = io.BytesIO()
+    pil_image.save(img_byte_arr, format='PNG')
+    
+    # Generate a unique ID for the image
+    unique_id = str(uuid.uuid4())
+    img_filename = f"{unique_id}.png"
+
+    # Save the image to a BytesIO object
+    image_bytes = img_byte_arr.getvalue()
+
+    # Upload the image to the correct label directory in the Hugging Face dataset
+    label_dir = f"{label}/{img_filename}"
+    upload_file(
+        path_or_fileobj=io.BytesIO(image_bytes),
+        path_in_repo=label_dir,
+        repo_id=DATASET_NAME,
+        repo_type="dataset",
+        token=HF_TOKEN,
+        commit_message=f"Add image with label {label}"
+    )
+
+    return "Thank you for your contribution to the open-source world! Your feedback helps us all move towards a clearer future"
+
 def replace_label_with_full_name(res, defect_dict_key_code):
     new_res = {}
     for dic in res:
@@ -163,22 +199,24 @@ def classify_image(image):
     # Extract labels and scores
     return replace_label_with_full_name(res, defect_dict)
 
+
 # Create the Gradio interface
-iface = gr.Interface(
-    classify_image, 
-    "image", 
-    "label", 
-    examples=[
-        ["examples/PP.jpg"],
-        ["examples/CS.jpg"], 
-        ["examples/GI.jpg"],
-        ["examples/RC.jpg"]
-    ],
-    description="Upload an image to classify its material.",
-    title="Defects Classification with AI by Subterra",
-    allow_flagging="manual",
-    flagging_options=defect_full_names_list
-)
-
-# Launch the interface
-iface.launch()
+with gr.Blocks() as demo:
+    gr.Markdown("# Defects Classification with AI by Subterra")
+    gr.Markdown("Upload an image to view a classification demonstration leveraging the dataset/library of images collected by WRc & United Utilities during The Water Services Regulation Authority (OFWAT) Innovation Challenge – Artificial Intelligence and Sewers. Not only can you see the initial classification, but you as the user can also inform us if the classification is correct. Your response will be used to retrain this model. The team at Subterra would like to thank all of those involved in collecting this dataset as we hope that other groups will use it to further advance technology solutions for the water industry.")
+
+    with gr.Row():
+        with gr.Column():
+            img_input = gr.Image()
+            submit_button = gr.Button("Classify")
+            examples = gr.Examples(["examples/CS.jpg", "examples/GI.jpg", "examples/PP.jpg", "examples/RC.jpg"], label = "Explore Examples", inputs=img_input)
+        with gr.Column():
+            output_label = gr.Label()
+            flagging_options = gr.Radio(defect_full_names_list, label="Does this classification look off to you? Your sharp eyes can help correct it. Flag any inaccuracies and suggest the right label!")
+            flag_button = gr.Button("Flag")
+            flag_status = gr.Textbox(label = "Every flag you submit polishes our dataset. Thanks for being an active participant in our open-source journey.",visible=True)
+
+    submit_button.click(classify_image, inputs=img_input, outputs=output_label)
+    flag_button.click(simple_flag, inputs=[img_input, flagging_options], outputs=flag_status)
+
+demo.launch()