added u7 evaluation

app.py

@@ -5,6 +5,7 @@ import requests
 import re
 import pandas as pd
 from huggingface_hub import ModelCard
+import os 
 
 
 def pass_emoji(passed):
@@ -15,6 +16,9 @@ def pass_emoji(passed):
     return passed
 
 api = HfApi()
+USERNAMES_DATASET_ID = "huggingface-course/audio-course-u7-hands-on"
+HF_TOKEN = os.environ.get("HF_TOKEN")
+U7_USERNAMES = hf_hub_download(USERNAMES_DATASET_ID, repo_type = "dataset", filename="usernames.csv", token=HF_TOKEN)
 
 
 def get_user_models(hf_username, task):
@@ -37,9 +41,9 @@ def get_user_models(hf_username, task):
 
     dataset_specific_models = []
 
-    if dataset == "": 
+    if dataset == "":
       return user_model_ids
-    else: 
+    else:
         for model in user_model_ids:
           meta = get_metadata(model)
           if meta is None:
@@ -47,11 +51,10 @@ def get_user_models(hf_username, task):
           try:
             if meta["datasets"] == [dataset]:
                 dataset_specific_models.append(model)
-          except: 
+          except:
             continue
         return dataset_specific_models
 
-
 def calculate_best_result(user_models, task):
   """
   Calculate the best results of a unit for a given task
@@ -155,9 +158,9 @@ def certification(hf_username):
           "passed_": False
   },
   {
-          "unit": "Unit 7: TBD",
-          "task": "TBD",
-          "baseline_metric": 0.99,
+          "unit": "Unit 7: Audio applications",
+          "task": "demo",
+          "baseline_metric": 0,
           "best_result": 0,
           "best_model_id": "",
           "passed_": False
@@ -191,13 +194,19 @@ def certification(hf_username):
       case "text-to-speech":
         try:
           user_tts_models = get_user_models(hf_username, task = "text-to-speech")
-          if user_tts_models: 
+          if user_tts_models:
             unit["best_result"] = 0
             unit["best_model_id"] = user_tts_models[0]
             unit["passed_"] = True
             unit["passed"] = pass_emoji(unit["passed_"])
         except: print("Either no relevant models found, or no metrics in the model card for automatic speech recognition")
-        print("Evaluation for this unit is work in progress")
+      case "demo":
+        u7_users = pd.read_csv(U7_USERNAMES)
+        if hf_username in u7_users['username']:
+            unit["best_result"] = 0
+            unit["best_model_id"] = "Demo check passed, no model id"
+            unit["passed_"] = True
+            unit["passed"] = pass_emoji(unit["passed_"])
       case _:
         print("Unknown task")
 
@@ -205,23 +214,25 @@ def certification(hf_username):
 
   df = pd.DataFrame(results_certification)
   df = df[['passed', 'unit', 'task', 'baseline_metric', 'best_result', 'best_model_id']]
-  return df
-    
+  return df    
+
 with gr.Blocks() as demo:
     gr.Markdown(f"""
     # 🏆 Check your progress in the Audio Course 🏆
-    
+
     - To get a certificate of completion, you must **pass 3 out of 4 assignments before July 31st 2023**.
     - To get an honors certificate, you must **pass 4 out of 4 assignments before July 31st 2023**.
 
-    To pass an assignment, your model's metric should be equal to or higher than the baseline metric.
-    
-    Make sure that you have uploaded your model(s) to Hub and type your Hugging Face Username here to check if you pass (in my case MariaK)
+    For the assignments where you have to train a model, your model's metric should be equal to or better than the baseline metric.
+    For the Unit 7 assignment, first, check your demo with Unit 7 assessment Space: https://huggingface.co/spaces/huggingface-course/audio-course-u7-assessment
+
+    Make sure that you have uploaded your model(s) to Hub, and that your Unit 7 demo is public.
+    To check your progress, type your Hugging Face Username here (in my case MariaK)
     """)
-    
+
     hf_username = gr.Textbox(placeholder="MariaK", label="Your Hugging Face Username")
     check_progress_button = gr.Button(value="Check my progress")
-    output = gr.components.Dataframe(value=certification(hf_username))    
+    output = gr.components.Dataframe(value=certification(hf_username))
     check_progress_button.click(fn=certification, inputs=hf_username, outputs=output)
 
 demo.launch()