Update app.py

app.py

@@ -66,31 +66,35 @@ def show_combined_repo_and_llm():
     llm_json = parse_llm_json_response(llm_output)
     # Update CSV for the current repo id
     csv_filename = "repo_ids.csv"
+    extraction_status = ""
+    strengths = ""
+    weaknesses = ""
     try:
         df = pd.read_csv(csv_filename)
-        highlight_idx = None
         # Cast columns to string to avoid dtype issues
         for col in ["strength", "weaknesses", "speciality", "relevance rating"]:
             df[col] = df[col].astype(str)
         for idx, row in df.iterrows():
             if row["repo id"] == repo_id:
-                highlight_idx = idx
-                if isinstance(llm_json, dict):
-                    df.at[idx, "strength"] = llm_json.get("strength", "")
-                    df.at[idx, "weaknesses"] = llm_json.get("weaknesses", "")
+                if isinstance(llm_json, dict) and "error" not in llm_json:
+                    extraction_status = "JSON extraction: SUCCESS"
+                    strengths = llm_json.get("strength", "")
+                    weaknesses = llm_json.get("weaknesses", "")
+                    df.at[idx, "strength"] = strengths
+                    df.at[idx, "weaknesses"] = weaknesses
                     df.at[idx, "speciality"] = llm_json.get("speciality", "")
                     df.at[idx, "relevance rating"] = llm_json.get("relevance rating", "")
+                else:
+                    extraction_status = f"JSON extraction: FAILED\nRaw: {llm_json.get('raw', '') if isinstance(llm_json, dict) else llm_json}"
                 break
-        # Add Selected column for highlighting
-        df["Selected"] = ""
-        if highlight_idx is not None:
-            df.at[highlight_idx, "Selected"] = "✅"
         df.to_csv(csv_filename, index=False)
     except Exception as e:
         df = pd.read_csv(csv_filename)
+        extraction_status = f"CSV update error: {e}"
     # Move to next repo for next click
     current_repo_idx += 1
-    return combined_content, llm_output, df
+    summary = f"{extraction_status}\n\nStrengths:\n{strengths}\n\nWeaknesses:\n{weaknesses}"
+    return combined_content, summary, df
 
 repo_id_input = gr.Textbox(label="Enter repo IDs (comma or newline separated)", lines=5, placeholder="repo1, repo2\nrepo3")
 df_output = gr.Dataframe(headers=["repo id", "strength", "weaknesses", "speciality", "relevance rating", "Usecase"])
@@ -108,7 +112,7 @@ with gr.Blocks() as demo:
     combined_txt = gr.Textbox(label="Combined Repo Files", lines=20)
     llm_output_txt = gr.Textbox(label="LLM Analysis Output", lines=10)
     df_display = gr.Dataframe(
-        headers=["repo id", "strength", "weaknesses", "speciality", "relevance rating", "Usecase", "Selected"]
+        headers=["repo id", "strength", "weaknesses", "speciality", "relevance rating", "Usecase"]
     )
     combine_btn.click(show_combined_repo_and_llm, inputs=None, outputs=[combined_txt, llm_output_txt, df_display])