Update app.py

app.py

@@ -2203,28 +2203,14 @@ Now, produce the report please.
     # Post-processing
     report = re.sub(r'\{\[\{(.*?)\}\]\}', r'\1', report)
     report = re.sub(r'\[\{(.*?)\}\]', r'\1', report)
-
-    
     
     # If the report is too long, compress it.
     if len(report) > MAX_MESSAGE_LENGTH:
-        report = compress_text(report, MAX_MESSAGE_LENGTH)    # Generate the reference table from the actual references_list
-    # Ref table
-    ref_table_html = generate_reference_table(references)
-    soup = BeautifulSoup(report, "html.parser")
-    ref_heading = soup.find(lambda tag: tag.name in ["h1", "h2", "h3", "h4"] and "Reference Summary Table" in tag.get_text())
-    if ref_heading:
-        next_sibling = ref_heading.find_next_sibling()
-        if next_sibling:
-            next_sibling.replace_with(BeautifulSoup(ref_table_html, "html.parser"))
-        else:
-            # If not found, append below the heading:
-            ref_heading.insert_after(BeautifulSoup(ref_table_html, "html.parser"))
-        report = str(soup)
-    else:
-        # Alternatively, append the table at the end of the report
-        report += "<h2>Reference Summary Table</h2>" + ref_table_html
-
+        report = compress_text(report, MAX_MESSAGE_LENGTH)
+    if report.startswith("Error calling OpenAI API"):
+        logging.error(f"generate_final_report error: {report}")
+        return f"Error generating report: {report}"
+    logging.info("generate_final_report: Report generated successfully.")
     return report
 
 def filter_search_results(results: list, visited_urls: set, query: str, clarifications: str) -> list:
@@ -2340,6 +2326,7 @@ Now generate the result.
     """
     messages = []  # Use prompt directly in openai_call, messages is handled there.
     llm_response = openai_call(prompt=prompt, messages=messages, model="o3-mini", temperature=0, max_tokens_param=50)
+    logging.info(f"Generated query tree: {llm_response}")
     cleaned_response = llm_response.strip()
     if cleaned_response.startswith("```"):
         cleaned_response = cleaned_response.strip("`").strip()
@@ -2347,7 +2334,7 @@ Now generate the result.
         queries = json.loads(cleaned_response)['queries']
         final_queries = queries[:min(len(queries), breadth)]  # Ensures the output respect the breadth parameter.
     except (json.JSONDecodeError, KeyError, TypeError) as e:
-        logging.error(f"Error parsing LLM response: {e}")
+        logging.error(f"Error parsing LLM response in generate_query_tree: {e}")
         final_queries = []  # Return empty list if parsing fails
         return final_queries
 
@@ -2367,6 +2354,7 @@ Output either:
 Output only the result.
 """
     languages_detected = openai_call(prompt, model="gpt-4o-mini", temperature=0, max_tokens_param=50)
+    logging.info(f"languages detected: {languages_detected}")
     if languages_detected != "No local attributes detected":
         queries = [make_multilingual_query(q, context, languages_detected) for q in queries]
     if not selected_engines or len(selected_engines) == 0: