For now, plot a summary for all repos

evaluations/documentation.py

@@ -1,4 +1,4 @@
-from .utils import log
+from .utils import log,fetch_code
 import re
 import numpy as np
 
@@ -21,6 +21,12 @@ def evaluate(verbose, llm, zip, readme):
           log(verbose, "LOG", "Readme file contains python examples.")
           overall = "Yes"
           return overall
+      
+      if (llm):
+          code = fetch_code(zip)
+          if (llm):
+            summary = llm.predict("HELP", f"{code}\nBased on the readme file above can you give a quick summary of this repository? Please use references to file names on the repository.")
+            log(verbose, "LOG", f"Based on the code, your readme file could be something like...\n{summary}")
 
       if (llm):
           prompt = f'{readme}\n \

evaluations/repo_evaluations.py

@@ -42,11 +42,6 @@ def evaluate(llm, verbose, repo_url, title=None, year=None):
       zip = zipfile.ZipFile(repository_zip_name)
       readme = fetch_readme(zip)
 
-      if (llm):
-        if (readme != ""):
-          summary = llm.predict("HELP", f"{readme}\nBased on the readme file above can you give a quick summary of this repository?")
-          log(verbose, "LOG", f"Summary: {summary}")
-
       results["pred_stars"] = fetch_repo_stars(verbose, repo_url, token)
 
       results["pred_license"] = license.evaluate(verbose, llm, zip, readme)