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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+images/intro.jpg filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,10 +1,10 @@
 ---
-title: HumanStudy Dogs
-emoji: 🏢
-colorFrom: pink
-colorTo: yellow
+title: PCNN-StanfordDogs-HumanStudy
+emoji: 🐕
+colorFrom: green
+colorTo: red
 sdk: gradio
-sdk_version: 4.31.1
+sdk_version: 4.29.0
 app_file: app.py
 pinned: false
 ---

app.py

@@ -0,0 +1,426 @@
+import json
+import gradio as gr
+import numpy as np
+import time
+import csv
+import json
+import os
+import random
+import string
+import sys
+import time
+import gradio as gr
+import numpy as np
+import pandas as pd
+from huggingface_hub import (
+    CommitScheduler,
+    HfApi,
+    InferenceClient,
+    login,
+    snapshot_download,
+    hf_hub_download,
+)
+from PIL import Image
+from utils import string_to_image
+import matplotlib.backends.backend_agg as agg
+import math
+from pathlib import Path
+import zipfile
+import gdown
+
+random.seed(int(time.time()))
+np.random.seed(int(time.time()))
+csv.field_size_limit(sys.maxsize)
+
+
+###############################################################################################################
+session_token = os.environ.get("SessionToken")
+login(token=session_token, add_to_git_credential=True)
+
+NUMBER_OF_IMAGES = 30
+intro_screen = Image.open("./images/intro.jpg")
+
+
+meta_top1 = json.load(open("./dogs/top1/metadata.json"))
+meta_topk = json.load(open("./dogs/topk/metadata.json"))
+
+all_data = {}
+all_data["top1"] = meta_top1
+all_data["topk"] = meta_topk
+
+
+# for data in  all_data["top1"] and all_data["topk"] add a key to show which type they are
+for k in all_data["top1"].keys():
+    all_data["top1"][k]["type"] = "top1"
+
+for k in all_data["topk"].keys():
+    all_data["topk"][k]["type"] = "topk"
+
+
+REPO_URL = "xairesearch2023-advnet/HumanStudyData-Dogs"
+JSON_DATASET_DIR = Path("responses")
+
+################################################################################################################
+
+scheduler = CommitScheduler(
+    repo_id=REPO_URL,
+    repo_type="dataset",
+    folder_path=JSON_DATASET_DIR,
+    path_in_repo="./data",
+    every=1,
+    private=True,
+)
+
+
+if not JSON_DATASET_DIR.exists():
+    JSON_DATASET_DIR.mkdir()
+
+
+def generate_data(type_of_nns, seed):
+    global NUMBER_OF_IMAGES
+    random.seed(int(seed))  # Set the random seed
+    keys = list(all_data[type_of_nns].keys())
+    sample_data = random.sample(keys, NUMBER_OF_IMAGES)
+
+    data = []
+    for k in sample_data:
+        new_datapoint = all_data[type_of_nns][k]
+        new_datapoint["image-path"] = f"./dogs/{type_of_nns}/{k}.jpeg"
+        data.append(new_datapoint)
+
+    return data
+
+
+def load_sample(data, current_index):
+    current_datapoint = data[current_index]
+
+    image_path = current_datapoint["image-path"]
+    image = Image.open(image_path)
+    top_1 = current_datapoint["top1-label"]
+    top_1_score = current_datapoint["top1-score"]
+
+    q_template = (
+        "<div style='font-size: 24px;'>Sam guessed the Input image is "
+        "<span style='font-weight: bold;'>{}</span> "
+        "with <span style='font-weight: bold;'>{}%</span> "
+        "confidence. Is this dog a <span style='font-weight: bold;'>{}</span>?"
+        "</div>"
+    )
+
+    q_template = (
+        "<div style='font-size: 24px;'>Sam guessed the Input image is "
+        "<span style='font-weight: bold;'>{}</span> "
+        "with <span style='font-weight: bold;'>{}%</span> "
+        "confidence.<br>Is this dog a <span style='font-weight: bold;'>{}</span>?"
+        "</div>"
+    )
+
+    top_1_score = top_1_score * 100
+    top_1_score = round(top_1_score, 2)
+
+    rounded_up_score = math.ceil(top_1_score)
+    rounded_up_score = int(rounded_up_score)
+    question = q_template.format(
+        top_1.replace("_", " "), str(rounded_up_score), top_1.replace("_", " ")
+    )
+
+    accept_reject = current_datapoint["Accept/Reject"]
+
+    return image, top_1, rounded_up_score, question, accept_reject
+
+
+def preprocessing(data, type_of_nns, current_index, history, username, seed):
+    print("preprocessing")
+    data = generate_data(type_of_nns, seed)  # Pass the seed here
+    print("data generated")
+
+    # set random seed to time
+    random.seed(int(time.time()))
+    # append a random text to the username
+    random_text = "".join(
+        random.choice(string.ascii_lowercase + string.digits) for _ in range(8)
+    )
+
+    if username == "":
+        username = "username"
+
+    username = f"{username}-{random_text}"
+
+    current_index = 0
+    print("loading sample ....")
+    qimage, top_1, top_1_score, question, accept_reject = load_sample(
+        data, current_index
+    )
+
+    return (
+        qimage,
+        top_1,
+        top_1_score,
+        question,
+        accept_reject,
+        current_index,
+        history,
+        data,
+        username,
+    )
+
+
+def update_app(decision, data, current_index, history, username):
+    global NUMBER_OF_IMAGES
+    if current_index == -1:
+        gr.Error("Please Enter your username and load samples")
+
+        fake_plot = string_to_image("Please Enter your username and load samples")
+        canvas = agg.FigureCanvasAgg(fake_plot)
+        canvas.draw()
+        empty_image = Image.frombytes(
+            "RGBA", canvas.get_width_height(), canvas.tostring_argb()
+        )
+
+        return (
+            empty_image,
+            "",
+            "",
+            "",
+            "",
+            current_index,
+            history,
+            data,
+            0,
+            gr.update(interactive=False),
+            gr.update(interactive=False),
+            "",
+        )
+
+    # Done, let's save and upload
+    if current_index == NUMBER_OF_IMAGES - 1:
+        time_stamp = int(time.time())
+
+        # Add decision to the history
+        current_dicitonary = data[current_index].copy()
+        current_dicitonary["user_decision"] = decision
+        current_dicitonary["user_id"] = username
+        accept_reject_string = "Accept" if decision == "YES" else "Reject"
+        current_dicitonary["is_user_correct"] = (
+            current_dicitonary["Accept/Reject"] == accept_reject_string
+        )
+        history.append(current_dicitonary)
+
+        # convert to percentage
+        final_decision_data = {
+            "user_id": username,
+            "time": time_stamp,
+            "history": history,
+        }
+
+        # upload the decision to the server
+        temp_filename = f"./responses/results_{username}.json"
+        # convert decision_dict to json and save it on the disk
+        with open(temp_filename, "w") as f:
+            json.dump(final_decision_data, f)
+
+        fake_plot = string_to_image("Thank you for your time!")
+        canvas = agg.FigureCanvasAgg(fake_plot)
+        canvas.draw()
+        empty_image = Image.frombytes(
+            "RGBA", canvas.get_width_height(), canvas.tostring_argb()
+        )
+
+        # TODO, Call the accuracy and show it to the user
+        # calcualte the mean of is_user_correct
+        all_is_user_correct = [d["is_user_correct"] for d in history]
+        accuracy = np.mean(all_is_user_correct) * 100
+        accuracy = round(accuracy, 2)
+
+        return (
+            empty_image,
+            "",
+            "",
+            "",
+            "",
+            current_index,
+            history,
+            data,
+            current_index + 1,
+            gr.update(interactive=False),
+            gr.update(interactive=False),
+            f"User Accuracy: {accuracy}",
+        )
+
+    if current_index >= 0 and current_index < NUMBER_OF_IMAGES - 1:
+        current_dicitonary = data[current_index].copy()
+        current_dicitonary["user_decision"] = decision
+        current_dicitonary["user_id"] = username
+        accept_reject_string = True if decision == "YES" else False
+        current_dicitonary["is_user_correct"] = (
+            current_dicitonary["Accept/Reject"] == accept_reject_string
+        )
+
+        print(f" accept/reject : {current_dicitonary['Accept/Reject'] }")
+        print(
+            f" accept/reject status: {current_dicitonary['Accept/Reject'] == accept_reject_string}"
+        )
+
+        history.append(current_dicitonary)
+
+        current_index += 1
+        qimage, top_1, top_1_score, question, accept_reject = load_sample(
+            data, current_index
+        )
+
+        return (
+            qimage,
+            top_1,
+            top_1_score,
+            question,
+            accept_reject,
+            current_index,
+            history,
+            data,
+            current_index,
+            gr.update(interactive=True),
+            gr.update(interactive=True),
+            "",
+        )
+
+
+def disable_component():
+    return gr.update(interactive=False)
+
+
+def enable_component():
+    return gr.update(interactive=True)
+
+
+def hide_component():
+    return gr.update(visible=False)
+
+
+with gr.Blocks(theme=gr.themes.Soft()) as demo:
+    data_state = gr.State({})
+    current_index = gr.State(-1)
+    history = gr.State([])
+
+    gr.Markdown("# Advising Networks")
+    gr.Markdown("## Accept/Reject AI predicted label using Explanations")
+
+    with gr.Column():
+        with gr.Row():
+            username_textbox = gr.Textbox(label="Username", value=f"username")
+            labeled_images_textbox = gr.Textbox(label="Labeled Images", value="0")
+            total_images_textbox = gr.Textbox(
+                label="Total Images", value=NUMBER_OF_IMAGES
+            )
+            type_of_nns_dropdown = gr.Dropdown(
+                label="Type of NNs",
+                choices=["top1", "topk"],
+                value="top1",
+            )
+            random_seed_textbox = gr.Number(label="Random Seed", value="0")
+
+        prepare_btn = gr.Button(value="Start The Experiment")
+
+    with gr.Column():
+        with gr.Row():
+            question_textbox = gr.HTML("")
+            # question_textbox = gr.Markdown("")
+
+        with gr.Column(elem_id="parent_row"):
+            query_image = gr.Image(
+                type="pil", label="Query", show_label=False, value="./images/intro.jpg"
+            )
+
+        with gr.Row():
+            accept_btn = gr.Button(value="YES", interactive=False)
+            reject_btn = gr.Button(value="NO", interactive=False)
+
+        with gr.Column(elem_id="parent_row"):
+            top_1_textbox = gr.Textbox(label="Top 1", value="", visible=False)
+            top_1_score_textbox = gr.Textbox(
+                label="Top 1 Score", value="", visible=False
+            )
+            accept_reject_textbox = gr.Textbox(
+                label="Accept/Reject", value="", visible=False
+            )
+
+    with gr.Column():
+        with gr.Row():
+            final_results = gr.HTML("")
+
+    # data, type_of_nns, current_index, history
+    prepare_btn.click(
+        preprocessing,
+        inputs=[
+            data_state,
+            type_of_nns_dropdown,
+            current_index,
+            history,
+            username_textbox,
+            random_seed_textbox,
+        ],
+        outputs=[
+            query_image,
+            top_1_textbox,
+            top_1_score_textbox,
+            question_textbox,
+            accept_reject_textbox,
+            current_index,
+            history,
+            data_state,
+            username_textbox,
+        ],
+    ).then(fn=disable_component, outputs=[prepare_btn]).then(
+        fn=disable_component, outputs=[type_of_nns_dropdown]
+    ).then(
+        fn=disable_component, outputs=[username_textbox]
+    ).then(
+        fn=disable_component, outputs=[prepare_btn]
+    ).then(
+        fn=enable_component, outputs=[accept_btn]
+    ).then(
+        fn=enable_component, outputs=[reject_btn]
+    ).then(
+        fn=hide_component, outputs=[prepare_btn]
+    )
+
+    accept_btn.click(
+        update_app,
+        inputs=[accept_btn, data_state, current_index, history, username_textbox],
+        outputs=[
+            query_image,
+            top_1_textbox,
+            top_1_score_textbox,
+            question_textbox,
+            accept_reject_textbox,
+            current_index,
+            history,
+            data_state,
+            labeled_images_textbox,
+            accept_btn,
+            reject_btn,
+            final_results,
+        ],
+    )
+
+    reject_btn.click(
+        update_app,
+        inputs=[reject_btn, data_state, current_index, history, username_textbox],
+        outputs=[
+            query_image,
+            top_1_textbox,
+            top_1_score_textbox,
+            question_textbox,
+            accept_reject_textbox,
+            current_index,
+            history,
+            data_state,
+            labeled_images_textbox,
+            accept_btn,
+            reject_btn,
+            final_results,
+        ],
+    )
+
+
+demo.launch(debug=False, server_name="0.0.0.0")
+# demo.launch(debug=False)