Update app.py

app.py

@@ -10,6 +10,8 @@ from utils import *
 
 api = HfApi()
 
+DEFAULT_HF_USERNAME = "giri1619"
+
 def get_user_models(hf_username, env_tag, lib_tag):
     """
     List the Reinforcement Learning models
@@ -126,13 +128,17 @@ def check_if_passed(model):
     model["passed_"] = True
 
 def certification(hf_username):
+  # Fall back to the default username if nothing was typed in the textbox
+  if not hf_username:
+      hf_username = DEFAULT_HF_USERNAME
+
   results_certification = [
       {
           "unit": "Unit 1",
           "env": "LunarLander-v2",
           "library": "stable-baselines3",
           "min_result": 200,
-          "best_result": 0,
+          "best_result": 200,
           "best_model_id": "",
           "passed_": False
       },
@@ -141,7 +147,7 @@ def certification(hf_username):
           "env": "Taxi-v3",
           "library": "q-learning",
           "min_result": 4,
-          "best_result": 0,
+          "best_result": 4,
           "best_model_id": "",
           "passed_": False
   },
@@ -150,7 +156,7 @@ def certification(hf_username):
           "env": "SpaceInvadersNoFrameskip-v4",
           "library": "stable-baselines3",
           "min_result": 200,
-          "best_result": 0,
+          "best_result": 200,
           "best_model_id": "",
           "passed_": False
   },
@@ -159,7 +165,7 @@ def certification(hf_username):
           "env": "CartPole-v1",
           "library": "reinforce",
           "min_result": 350,
-          "best_result": 0,
+          "best_result": 350,
           "best_model_id": "",
           "passed_": False
   },
@@ -168,7 +174,7 @@ def certification(hf_username):
           "env": "Pixelcopter-PLE-v0",
           "library": "reinforce",
           "min_result": 5,
-          "best_result": 0,
+          "best_result": 5,
           "best_model_id": "",
           "passed_": False
     },
@@ -177,7 +183,7 @@ def certification(hf_username):
           "env": "ML-Agents-SnowballTarget",
           "library": "ml-agents",
           "min_result": -100,
-          "best_result": 0,
+          "best_result": -100,
           "best_model_id": "",
           "passed_": False
     },
@@ -186,7 +192,7 @@ def certification(hf_username):
           "env": "ML-Agents-Pyramids",
           "library": "ml-agents",
           "min_result": -100,
-          "best_result": 0,
+          "best_result": -100,
           "best_model_id": "",
           "passed_": False
     },
@@ -195,7 +201,7 @@ def certification(hf_username):
           "env": "PandaReachDense",
           "library": "stable-baselines3",
           "min_result": -3.5,
-          "best_result": 0,
+          "best_result": -3.5,
           "best_model_id": "",
           "passed_": False
     },
@@ -204,7 +210,7 @@ def certification(hf_username):
           "env": "ML-Agents-SoccerTwos",
           "library": "ml-agents",
           "min_result": -100,
-          "best_result": 0,
+          "best_result": -100,
           "best_model_id": "",
           "passed_": False
     },
@@ -213,7 +219,7 @@ def certification(hf_username):
           "env": "LunarLander-v2",
           "library": "deep-rl-course",
           "min_result": -500,
-          "best_result": 0,
+          "best_result": -500,
           "best_model_id": "",
           "passed_": False
     },
@@ -222,7 +228,7 @@ def certification(hf_username):
           "env": "doom_health_gathering_supreme",
           "library": "sample-factory",
           "min_result": 5,
-          "best_result": 0,
+          "best_result": 5,
           "best_model_id": "",
           "passed_": False
     },
@@ -267,20 +273,17 @@ with gr.Blocks() as demo:
     
     - To get a certificate of completion, you must **pass 80% of the assignments**.
     - To get an honors certificate, you must **pass 100% of the assignments**.
-
     There's **no deadlines, the course is self-paced**.
-
     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)
+    Just type your Hugging Face Username 🤗 (in my case giri1619)
     """)
     
-    hf_username = gr.Textbox(placeholder="ThomasSimonini", label="Your Hugging Face Username")
+    hf_username = gr.Textbox(value=DEFAULT_HF_USERNAME, placeholder="giri1619", 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"])
+    output = gr.components.Dataframe(value=certification(DEFAULT_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()