Duplicate from ThomasSimonini/Check-my-progress-Deep-RL-Course

Co-authored-by: Thomas Simonini <ThomasSimonini@users.noreply.huggingface.co>

.gitattributes

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README.md

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+---
+title: Check My Progress Deep RL Course
+emoji: 👀
+colorFrom: yellow
+colorTo: purple
+sdk: gradio
+sdk_version: 3.16.0
+app_file: app.py
+pinned: false
+duplicated_from: ThomasSimonini/Check-my-progress-Deep-RL-Course
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import gradio as gr
+from huggingface_hub import HfApi, hf_hub_download
+from huggingface_hub.repocard import metadata_load
+
+import pandas as pd
+
+from utils import *
+
+api = HfApi()
+
+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):
+    api = HfApi()
+    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):
+  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": "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 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)
+ 
+  df = pd.DataFrame(results_certification)
+  df = df[['passed', 'unit', 'env', 'min_result', 'best_result', 'best_model_id']]
+  return df
+
+
+with gr.Blocks() as demo:
+    gr.Markdown(f"""
+    # 🏆 Check your progress in the Deep Reinforcement Learning Course 🏆
+    You can check your progress here.
+    
+    - To get a certificate of completion, you must **pass 80% of the assignments before June 1st 2023**.
+    - To get an honors certificate, you must **pass 100% of the assignments before June 1st 2023**.
+
+    To pass an assignment your model result (mean_reward - std_reward) must be >= min_result
+
+    **When min_result = -100 it means that you just need to push a model to pass this hands-on. No need to reach a certain result.**
+    
+    Just type your Hugging Face Username 🤗 (in my case ThomasSimonini)
+    """)
+    
+    hf_username = gr.Textbox(placeholder="ThomasSimonini", label="Your Hugging Face Username")
+    #email = gr.Textbox(placeholder="thomas.simonini@huggingface.co", label="Your Email (to receive your certificate)")
+    check_progress_button = gr.Button(value="Check my progress")
+    output = gr.components.Dataframe(value= certification(hf_username), headers=["Pass?", "Unit", "Environment", "Baseline", "Your best result", "Your best model id"], datatype=["markdown", "markdown", "markdown", "number", "number", "markdown", "bool"])
+    check_progress_button.click(fn=certification, inputs=hf_username, outputs=output)
+
+demo.launch()

utils.py

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+# Based on Omar Sanseviero work
+# Make model clickable link
+def make_clickable_model(model_name):
+    # remove user from model name
+    model_name_show = ' '.join(model_name.split('/')[1:])
+
+    link = "https://huggingface.co/" + model_name
+    return f'<a target="_blank" href="{link}">{model_name_show}</a>'
+
+def pass_emoji(passed):
+    print("PASSED", passed)
+    if passed is True:
+        passed = "✅"
+    else:
+        passed = "❌"
+    return passed