new logging style

app.py

@@ -6,10 +6,12 @@ st.write("\n")
 st.write("Welcome to the online reproducibility evaluation tool!")
 st.write("We follow guidelines provided by Simkó et al. (2022) (https://arxiv.org/abs/2210.11146)")
 
+checkbox = st.checkbox("Would you like to see recommendations during evaluation?", value=False)
 repo_link = st.text_input("Github repository link:", value="", type="default", help=None)
 
 if (repo_link):
-    evaluate(llm=None, verbose=2, repo_url=repo_link)
+    verbose = 3 if checkbox else 4
+    evaluate(llm=None, verbose=verbose, repo_url=repo_link)
 
 with st.form("my_form"):
     st.write("Notice something wrong? Please tell us so we can improve.")
@@ -20,7 +22,7 @@ with st.form("my_form"):
     if submitted:
         if repo_link and feedback:
             st.write("Repository:", repo_link)
-            
+
             google_form_url = "https://docs.google.com/forms/d/e/1FAIpQLSeJ6eWl4rdZX2lKnIMkylcMBR0qj2Dq2dDHzQX5dfUjOieB7w/formResponse"
             form_data = {
                 "entry.1379878677": repo_link,

evaluations/documentation.py

@@ -3,7 +3,7 @@ import re
 import numpy as np
 
 def evaluate(verbose, llm, zip, readme):
-  log(verbose, "LOG", "\nEvaluating code documentation...")
+  log(verbose, "TITLE", "\nEvaluating code documentation...")
   overall = "No"
 
 
@@ -14,7 +14,7 @@ def evaluate(verbose, llm, zip, readme):
   if (readme):
       non_empty_rows = [row for row in readme.split("\n") if row != ""]
       if (len(non_empty_rows) < 5):
-          log(verbose, "WARNING", "Readme file has very few lines")
+          log(verbose, "ERROR", "Readme file has very few lines")
           return overall
 
       if (count_code_lines(non_empty_rows) > 5):

evaluations/license.py

@@ -2,7 +2,7 @@ from .utils import log, model_predict
 import re
 
 def evaluate(verbose, llm, zip, readme):
-  log(verbose, "LOG", "\nEvaluating repository licensing...")
+  log(verbose, "TITLE", "\nEvaluating repository licensing...")
   overall = "No"
   license_files = [license for license in zip.namelist() if ((("LICENSE" in license) | ("license" in license)) & (len(license.split("/")) == 2))]
   if (len(license_files) > 0):

evaluations/repo_evaluations.py

@@ -92,7 +92,7 @@ def full_evaluation():
           row[column] = result[column]
 
       full_results.append(row)
-  return pd.dataFrame(full_results)
+  return pd.DataFrame(full_results)
 
 def midl_evaluations():
   compare_to_gt = True
@@ -145,4 +145,4 @@ def midl_evaluations():
               eval_licensing.append(("No" if row["license"] == "No" else "Yes") == row["pred_license"])
               print(f"LICENSE acc. - {row['pred_license']} (GT:{row['license']}) / {int(100 * np.mean(eval_licensing))}%")
 
-  return pd.dataFrame(full_results)
+  return pd.DataFrame(full_results)

evaluations/requirements.py

@@ -1,7 +1,7 @@
 from .utils import log
 
 def evaluate(verbose, llm, zip, readme):
-  log(verbose, "LOG", "\nLooking for package dependencies for running the code...")
+  log(verbose, "TITLE", "\nLooking for package dependencies for running the code...")
   overall = "No"
 
   scripts = [file_path for file_path in zip.namelist() if ((file_path.endswith(".py") | file_path.endswith(".ipynb")))]
@@ -13,7 +13,7 @@ def evaluate(verbose, llm, zip, readme):
       requirements = zip.open(file).read().decode("utf-8")
       overall = "Yes"
       if (len(requirements.split("\n")) < 5):
-          log(verbose, "WARNING", "Requirements file contains too few lines.")
+          log(verbose, "ERROR", "Requirements file contains too few lines.")
           overall = "No"
 
   if (readme):

evaluations/training.py

@@ -2,7 +2,7 @@ from .utils import log
 import re
 
 def evaluate(verbose, llm, zip, readme):
-  log(verbose, "LOG", "\nLooking for code to train the model...")
+  log(verbose, "TITLE", "\nLooking for code to train the model...")
   overall = "No"
 
 

evaluations/utils.py

@@ -105,32 +105,29 @@ def fetch_openalex(verbose, paper_name, year):
 def log(verbose, log_type, log_text, hf=False):
     if (verbose == 0):
         return
+    
+    if (verbose == 1 | verbose == 2):
+        show = print
+    if (verbose == 3 | verbose == 4):
+        show = st.write
 
     # Align line-break
     if (log_text.startswith("\n")):
-        if (verbose == 1):
-            print("\n")
-        if (verbose == 2):
-            st.write("\n")
+        show("\n")
         log_text = log_text.lstrip('\n')
 
-    if (log_type == "LOG"):
-        log_text = f"LOG: {log_text}"
-    if (log_type == "ERROR"):
-        log_text = f"ERROR: {log_text}"
-    if (log_type == "WARNING"):
-        log_text = f"WARNING: {log_text}"
-        
-
-    if (verbose == 1):
-        print(log_text)
-        return
-
-    if (verbose == 2):
-        st.write(log_text)
-        return
-
-    raise Exception(log_text)
+    # Only show tips in verbose mode 2 and 4
+    if ((log_type == "TITLE") & (verbose == 2 | verbose == 4)):
+        show(f"\n####{log_text}")
+    if ((log_type == "TIP") & (verbose == 2 | verbose == 4)):
+        show(f"*{log_text}*")
+    if ((log_type == "LOG") & (verbose == 2 | verbose == 4)):
+        show(f"{log_text}")
+    if ((log_type == "ERROR")):
+        show(f"**{log_text}**")
+
+    if ((log_type != "TIP") & (log_type != "LOG") & (log_type != "ERROR") & (log_type != "TITLE")):
+        raise ValueError("Invalid log type. Use 'TIP', 'LOG', 'TITLE' or 'ERROR'.")
 
 def init_llm(verbose):
   log(verbose, "LOG", "Initializing LLM...")

evaluations/validating.py

@@ -2,7 +2,7 @@ from .utils import log
 import re
 
 def evaluate(verbose, llm, zip, readme):
-  log(verbose, "LOG", "\nLooking for examples for running the model...")
+  log(verbose, "TITLE", "\nLooking for examples for running the model...")
   overall = "No"
   patterns = {
       'tensorflow': [

evaluations/weights.py

@@ -2,7 +2,7 @@ from .utils import log
 import re
 
 def evaluate(verbose, llm, zip, readme):
-  log(verbose, "LOG", "\nLooking for pre-trained model weights...")
+  log(verbose, "TITLE", "\nLooking for pre-trained model weights...")
   overall = "No"
   files = [file_path for file_path in zip.namelist() if ((file_path.endswith(".h5") | file_path.endswith(".pth") | file_path.endswith(".torch") | file_path.endswith(".pt") | file_path.endswith(".tar.gz") | file_path.endswith("checkpoint.pt") | ("weights" in file_path) | file_path.endswith("ckpt")))]
   if (len(files) > 0):