Update app.py

app.py

@@ -218,7 +218,7 @@ def certification(hf_username, first_name, last_name):
           "unit": "Unit 8 PI",
           "env": "GodotRL-JumperHard",
           "library": "cleanrl",
-          "min_result": -100,
+          "min_result": 100,
           "best_result": 0,
           "best_model_id": "",
           "passed_": False
@@ -227,7 +227,7 @@ def certification(hf_username, first_name, last_name):
           "unit": "Unit 8 PII",
           "env": "Vizdoom-Battle",
           "library": "cleanrl",
-          "min_result": -100,
+          "min_result": 100,
           "best_result": 0,
           "best_model_id": "",
           "passed_": False
@@ -254,9 +254,9 @@ def certification(hf_username, first_name, last_name):
 
   df = df1[['passed', 'unit', 'env', 'min_result', 'best_result', 'best_model_id']]
 
-  verify_certification(results_certification, hf_username, first_name, last_name)
+  message, certification = verify_certification(results_certification, hf_username, first_name, last_name)
 
-  return df
+  return message, df, certification
 
 """
 Verify that the user pass.
@@ -281,27 +281,32 @@ def verify_certification(df, hf_username, first_name, last_name):
   
   if pass_percentage == 100:
     # Generate a certificate of excellence
-    generate_certificate("./certificate_models/certificate-excellence.png", first_name, last_name)
+    certificate = 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)
     
-    # Output everything in gradio
-
+    # Add a message
+    message = "Congratulations, you successfully completed the Hugging Face Deep Reinforcement Learning Course 🎉 \n Since you pass 100% of the hands-on you get a Certificate of Excellence 🎓."
 
   elif pass_percentage < 100 and pass_percentage >= 80:
     # Certificate of completion
-    generate_certificate("./certificate_models/certificate-completion.png", first_name, last_name)
+    certificate = 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)
 
-    # Output everything in gradio
-
+    # Add a message
+    message = "Congratulations, you successfully completed the Hugging Face Deep Reinforcement Learning Course 🎉 \n Since you pass 80% of the hands-on you get a Certificate of Completion 🎓. 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
-    print ("not pass yet")
+    certificate = ""
+    # 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. Check below which units you need to do again to get your certificate 💪"
+
+  return certificate, message
+    
 
 def generate_certificate(certificate_model, first_name, last_name):
     im = Image.open(certificate_model)
@@ -328,6 +333,8 @@ def generate_certificate(certificate_model, first_name, last_name):
   
     im.save("certificate_"+".png")
 
+    return im
+
 
 def add_certified_user(hf_username, first_name, last_name, pass_percentage):
   print("ADD CERTIFIED USER")
@@ -337,10 +344,7 @@ def add_certified_user(hf_username, first_name, last_name, pass_percentage):
   # Check if this hf_username is already in our dataset:
   check = history.loc[history['hf_username'] == hf_username]
   if not check.empty:
-    #print("CHECK", check)
-    #print("INDEX", check.index[0])
     history = history.drop(labels=check.index[0], axis=0)
-    #history.iloc[[check.index[0]],['hf_username', 'first_name', 'last_name', 'pass_percentage', 'datetime']] = hf_username, first_name, last_name, pass_percentage, time.time()
   
   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)
@@ -351,17 +355,15 @@ def add_certified_user(hf_username, first_name, last_name, pass_percentage):
 
 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 the end of April 2023**.
-    - To get an honors certificate, you must **pass 100% of the assignments before the end of April 2023**.
+    # Get your certificate 🎓
+    The certification process is completely free:
 
-    To pass an assignment your model result (mean_reward - std_reward) must be >= min_result
+    - 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**.
 
-    **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)
+    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)")
@@ -369,8 +371,9 @@ with gr.Blocks() as demo:
     last_name = gr.Textbox(placeholder="Doe", label="Your Last Name")
     #email = gr.Textbox(placeholder="[email protected]", label="Your Email (to receive your certificate)")
     check_progress_button = gr.Button(value="Check my progress")
-    output1 = gr.components.Dataframe(value= certification(hf_username, first_name, last_name), headers=["Pass?", "Unit", "Environment", "Baseline", "Your best result", "Your best model id"], datatype=["markdown", "markdown", "markdown", "number", "number", "markdown", "bool"])
-    #output2 = gr.components.Image(type="pil")
-    check_progress_button.click(fn=certification, inputs=[hf_username, first_name, last_name], outputs= output1)#[output1, output2])
+    output_text = gr.components.Text()
+    output_pdf = gr.components.Image(type="pil")
+    output_dataframe = gr.components.Dataframe(value= certification(hf_username, first_name, last_name), 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, first_name, last_name], outputs=[output_text, output_pdf, output_dataframe])#[output1, output2])
 
 demo.launch(debug=True)