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_user_sf_models(hf_username, env_tag, lib_tag): models_sf = [] models = api.list_models(author=hf_username, filter=["reinforcement-learning", lib_tag]) user_model_ids = [x.modelId for x in models] for model in user_model_ids: meta = get_metadata(model) if meta is None: continue result = meta["model-index"][0]["results"][0]["dataset"]["name"] if result == env_tag: models_sf.append(model) return models_sf 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 = -1000 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": "PandaReachDense", "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": "LunarLander-v2", "library": "deep-rl-course", "min_result": -500, "best_result": 0, "best_model_id": "", "passed_": False }, { "unit": "Unit 8 PII", "env": "doom_health_gathering_supreme", "library": "sample-factory", "min_result": 5, "best_result": 0, "best_model_id": "", "passed_": False }, ] for unit in results_certification: if unit["unit"] == "Unit 6": # Since Unit 6 can use PandaReachDense-v2 or v3 user_models = get_user_models(hf_username, "PandaReachDense-v3", unit["library"]) if len(user_models) == 0: print("Empty") user_models = get_user_models(hf_username, "PandaReachDense-v2", unit["library"]) elif unit["unit"] != "Unit 8 PII": # Get user model user_models = get_user_models(hf_username, unit['env'], unit['library']) # For sample factory vizdoom we don't have env tag for now else: user_models = get_user_sf_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, pass_ = verify_certification(results_certification, hf_username, first_name, last_name) print("MESSAGE", message) if pass_: visible = True else: visible = False return message, pdf, certificate, df, output_row.update(visible=visible) """ 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 pass_ = False for unit in df: if unit["passed_"] is True: model_pass_nb += 1 pass_percentage = (model_pass_nb/11) * 100 print("pass_percentage", pass_percentage) if pass_percentage == 100: pass_ = True # 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: pass_ = True # 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 = "./fail.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, pass_ 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**. - To get a *certificate of honors*: you need to **pass 100% of the assignments**. There's **no deadlines, the course is self-paced**. 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") output_text = gr.components.Textbox() with gr.Row(visible=True) as output_row: 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)