import gradio as gr from huggingface_hub import HfApi, hf_hub_download, Repository from huggingface_hub.repocard import metadata_load from PIL import Image, ImageDraw, ImageFont from datetime import date import time import os import pandas as pd from utils import * api = HfApi() DATASET_REPO_URL = "https://huggingface.co/datasets/huggingface-projects/Deep-RL-Course-Certification" CERTIFIED_USERS_FILENAME = "certified_users.csv" CERTIFIED_USERS_DIR = "certified_users" HF_TOKEN = os.environ.get("HF_TOKEN") repo = Repository( local_dir=CERTIFIED_USERS_DIR, clone_from=DATASET_REPO_URL, use_auth_token=HF_TOKEN ) def get_user_models(hf_username, env_tag, lib_tag): """ List the Reinforcement Learning models from user given environment and lib :param hf_username: User HF username :param env_tag: Environment tag :param lib_tag: Library tag """ api = HfApi() models = api.list_models(author=hf_username, filter=["reinforcement-learning", env_tag, lib_tag]) user_model_ids = [x.modelId for x in models] return user_model_ids def get_metadata(model_id): """ Get model metadata (contains evaluation data) :param model_id """ try: readme_path = hf_hub_download(model_id, filename="README.md") return metadata_load(readme_path) except requests.exceptions.HTTPError: # 404 README.md not found return None def parse_metrics_accuracy(meta): """ Get model results and parse it :param meta: model metadata """ if "model-index" not in meta: return None result = meta["model-index"][0]["results"] metrics = result[0]["metrics"] accuracy = metrics[0]["value"] return accuracy def parse_rewards(accuracy): """ Parse mean_reward and std_reward :param accuracy: model results """ default_std = -1000 default_reward= -1000 if accuracy != None: accuracy = str(accuracy) parsed = accuracy.split(' +/- ') if len(parsed)>1: mean_reward = float(parsed[0]) std_reward = float(parsed[1]) elif len(parsed)==1: #only mean reward mean_reward = float(parsed[0]) std_reward = float(0) else: mean_reward = float(default_std) std_reward = float(default_reward) else: mean_reward = float(default_std) std_reward = float(default_reward) return mean_reward, std_reward def calculate_best_result(user_model_ids): """ Calculate the best results of a unit best_result = mean_reward - std_reward :param user_model_ids: RL models of a user """ best_result = -100 best_model_id = "" for model in user_model_ids: meta = get_metadata(model) if meta is None: continue accuracy = parse_metrics_accuracy(meta) mean_reward, std_reward = parse_rewards(accuracy) result = mean_reward - std_reward if result > best_result: best_result = result best_model_id = model return best_result, best_model_id def check_if_passed(model): """ Check if result >= baseline to know if you pass :param model: user model """ if model["best_result"] >= model["min_result"]: model["passed_"] = True def certification(hf_username, first_name, last_name): results_certification = [ { "unit": "Unit 1", "env": "LunarLander-v2", "library": "stable-baselines3", "min_result": 200, "best_result": 0, "best_model_id": "", "passed_": False }, { "unit": "Unit 2", "env": "Taxi-v3", "library": "q-learning", "min_result": 4, "best_result": 0, "best_model_id": "", "passed_": False }, { "unit": "Unit 3", "env": "SpaceInvadersNoFrameskip-v4", "library": "stable-baselines3", "min_result": 200, "best_result": 0, "best_model_id": "", "passed_": False }, { "unit": "Unit 4", "env": "CartPole-v1", "library": "reinforce", "min_result": 350, "best_result": 0, "best_model_id": "", "passed_": False }, { "unit": "Unit 4", "env": "Pixelcopter-PLE-v0", "library": "reinforce", "min_result": 5, "best_result": 0, "best_model_id": "", "passed_": False }, { "unit": "Unit 5", "env": "ML-Agents-SnowballTarget", "library": "ml-agents", "min_result": -100, "best_result": 0, "best_model_id": "", "passed_": False }, { "unit": "Unit 5", "env": "ML-Agents-Pyramids", "library": "ml-agents", "min_result": -100, "best_result": 0, "best_model_id": "", "passed_": False }, { "unit": "Unit 6", "env": "AntBulletEnv-v0", "library": "stable-baselines3", "min_result": 650, "best_result": 0, "best_model_id": "", "passed_": False }, { "unit": "Unit 6", "env": "PandaReachDense-v2", "library": "stable-baselines3", "min_result": -3.5, "best_result": 0, "best_model_id": "", "passed_": False }, { "unit": "Unit 7", "env": "ML-Agents-SoccerTwos", "library": "ml-agents", "min_result": -100, "best_result": 0, "best_model_id": "", "passed_": False }, { "unit": "Unit 8 PI", "env": "GodotRL-JumperHard", "library": "cleanrl", "min_result": 100, "best_result": 0, "best_model_id": "", "passed_": False }, { "unit": "Unit 8 PII", "env": "Vizdoom-Battle", "library": "cleanrl", "min_result": 100, "best_result": 0, "best_model_id": "", "passed_": False }, ] for unit in results_certification: # Get user model user_models = get_user_models(hf_username, unit['env'], unit['library']) # Calculate the best result and get the best_model_id best_result, best_model_id = calculate_best_result(user_models) # Save best_result and best_model_id unit["best_result"] = best_result unit["best_model_id"] = make_clickable_model(best_model_id) # Based on best_result do we pass the unit? check_if_passed(unit) unit["passed"] = pass_emoji(unit["passed_"]) print(results_certification) df1 = pd.DataFrame(results_certification) df = df1[['passed', 'unit', 'env', 'min_result', 'best_result', 'best_model_id']] certificate, message, pdf = verify_certification(results_certification, hf_username, first_name, last_name) print("MESSAGE", message) return message, pdf, certificate, df #, output_row.update(visible=True) """ Verify that the user pass. If yes: - Generate the certification - Send an email - Print the certification If no: - Explain why the user didn't pass yet """ def verify_certification(df, hf_username, first_name, last_name): # Check that we pass model_pass_nb = 0 pass_percentage = 0 for unit in df: if unit["passed_"] is True: model_pass_nb += 1 pass_percentage = (model_pass_nb/12) * 100 print("pass_percentage", pass_percentage) if pass_percentage == 100: # Generate a certificate of excellence certificate, pdf = generate_certificate("./certificate_models/certificate-excellence.png", first_name, last_name) # Add this user to our database add_certified_user(hf_username, first_name, last_name, pass_percentage) # Add a message message = """ Congratulations, you successfully completed the Hugging Face Deep Reinforcement Learning Course πŸŽ‰! \n Since you pass 100% of the hands-on you get a Certificate of Excellence πŸŽ“. \n You can download your certificate below ⬇️ \n Don't hesitate to share your certificate image below on Twitter and Linkedin (you can tag me @ThomasSimonini and @huggingface) πŸ€— """ elif pass_percentage < 100 and pass_percentage >= 80: # Certificate of completion certificate, pdf = generate_certificate("./certificate_models/certificate-completion.png", first_name, last_name) # Add this user to our database add_certified_user(hf_username, first_name, last_name, pass_percentage) # Add a message message = """ Congratulations, you successfully completed the Hugging Face Deep Reinforcement Learning Course πŸŽ‰! \n Since you pass 80% of the hands-on you get a Certificate of Completion πŸŽ“. \n You can download your certificate below ⬇️ \n Don't hesitate to share your certificate image below on Twitter and Linkedin (you can tag me @ThomasSimonini and @huggingface) πŸ€— \n You can try to get a Certificate of Excellence if you pass 100% of the hands-on, don't hesitate to check which unit you didn't pass and update these models. """ else: # Not pass yet certificate = Image.new("RGB", (100, 100), (255, 255, 255)) pdf = "" # Add a message message = """ You didn't pass the minimum of 80% of the hands-on to get a certificate of completion. But don't be discouraged! \n Check below which units you need to do again to get your certificate πŸ’ͺ """ print("return certificate") return certificate, message, pdf def generate_certificate(certificate_model, first_name, last_name): im = Image.open(certificate_model) d = ImageDraw.Draw(im) name_font = ImageFont.truetype("Quattrocento-Regular.ttf", 100) date_font = ImageFont.truetype("Quattrocento-Regular.ttf", 48) name = str(first_name) + " " + str(last_name) print("NAME", name) # Debug line name #d.line(((200, 740), (1800, 740)), "gray") #d.line(((1000, 0), (1000, 1400)), "gray") # Name d.text((1000, 740), name, fill="black", anchor="mm", font=name_font) # Debug line date #d.line(((1500, 0), (1500, 1400)), "gray") # Date of certification d.text((1480, 1170), str(date.today()), fill="black", anchor="mm", font=date_font) pdf = im.convert('RGB') pdf.save('certificate.pdf') return im, "./certificate.pdf" def add_certified_user(hf_username, first_name, last_name, pass_percentage): """ Add the certified user to the database """ print("ADD CERTIFIED USER") repo.git_pull() history = pd.read_csv(os.path.join(CERTIFIED_USERS_DIR, CERTIFIED_USERS_FILENAME)) # Check if this hf_username is already in our dataset: check = history.loc[history['hf_username'] == hf_username] if not check.empty: history = history.drop(labels=check.index[0], axis=0) new_row = pd.DataFrame({'hf_username': hf_username, 'first_name': first_name, 'last_name': last_name, 'pass_percentage': pass_percentage, 'datetime': time.time()}, index=[0]) history = pd.concat([new_row, history[:]]).reset_index(drop=True) history.to_csv(os.path.join(CERTIFIED_USERS_DIR, CERTIFIED_USERS_FILENAME), index=False) repo.push_to_hub(commit_message="Update certified users list") with gr.Blocks() as demo: gr.Markdown(f""" # Get your Deep Reinforcement Learning Certificate πŸŽ“ The certification process is completely free: - To get a *certificate of completion*: you need to **pass 80% of the assignments before the end of April 2023**. - To get a *certificate of honors*: you need to **pass 100% of the assignments before the end of April 2023**. For more information about the certification process [check this](https://huggingface.co/deep-rl-course/communication/certification) Don’t hesitate to share your certificate on Twitter (tag me @ThomasSimonini and @huggingface) and on Linkedin. """) hf_username = gr.Textbox(placeholder="ThomasSimonini", label="Your Hugging Face Username (case sensitive)") first_name = gr.Textbox(placeholder="Jane", label="Your First Name") last_name = gr.Textbox(placeholder="Doe", label="Your Last Name") #email = gr.Textbox(placeholder="jane.doe@gmail.com", label="Your Email (to receive your certificate)") check_progress_button = gr.Button(value="Check if I pass") #with gr.Row(visible=False) as output_row: output_text = gr.components.Textbox() output_pdf = gr.File() output_img = gr.components.Image(type="pil") output_dataframe = gr.components.Dataframe(headers=["Pass?", "Unit", "Environment", "Baseline", "Your best result", "Your best model id"], datatype=["markdown", "markdown", "markdown", "number", "number", "markdown", "bool"]) #value= certification(hf_username, first_name, last_name), check_progress_button.click(fn=certification, inputs=[hf_username, first_name, last_name], outputs=[output_text, output_pdf, output_img, output_dataframe])#, output_row])#[output1, output2]) demo.launch(debug=True)