g

analyzer.py

@@ -115,7 +115,7 @@ def aggregate_chunk_analyses(chunk_jsons: list) -> str:
     client = OpenAI(api_key=os.getenv("modal_api"))
     client.base_url = os.getenv("base_url")
     aggregation_prompt = (
-        "You are a highly precise and strict JSON generator. You are given a list of JSON analyses of code chunks. "
+        "You are a highly precise and strict, code analyzer and  JSON generator. You are given a list of JSON analyses of code chunks. "
         "Aggregate these into a SINGLE overall JSON summary with the same keys: 'strength', 'weaknesses', 'speciality', 'relevance rating'. "
         "Summarize and combine the information from all chunks. Do NOT include any explanation, markdown, or text outside the JSON. "
         "If a key is missing in all chunks, use an empty string. "

app.py

@@ -105,7 +105,14 @@ def show_combined_repo_and_llm():
     except Exception as e:
         return f"Error reading {txt_path}: {e}", "", read_csv_as_text("repo_ids.csv")
     llm_output = analyze_combined_file(txt_path)
-    llm_json = parse_llm_json_response(llm_output)
+    # Extract only the last JSON object (final summary) for CSV writing
+    last_start = llm_output.rfind('{')
+    last_end = llm_output.rfind('}')
+    if last_start != -1 and last_end != -1 and last_end > last_start:
+        final_json_str = llm_output[last_start:last_end+1]
+    else:
+        final_json_str = llm_output
+    llm_json = parse_llm_json_response(final_json_str)
     # Update CSV for the current repo id
     csv_filename = "repo_ids.csv"
     extraction_status = ""