revert changes

app.py

@@ -110,7 +110,6 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     answers_payload = []
     print(f"Running agent on {len(questions_data)} questions...")
     for item in questions_data:
-        break
         task_id = item.get("task_id")
         question_text = item.get("question")
         if not task_id or question_text is None:
@@ -124,36 +123,12 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
              print(f"Error running agent on task {task_id}: {e}")
              results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": f"AGENT ERROR: {e}"})
 
-    # if not answers_payload:
-    #     print("Agent did not produce any answers to submit.")
-    #     return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
+    if not answers_payload:
+        print("Agent did not produce any answers to submit.")
+        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)
 
     # 4. Prepare Submission 
     submission_data = {"username": username.strip(), "agent_code": agent_code, "answers": answers_payload}
-    submission_data = {
-        'username': 'mrp-78',
-        'agent_code': 'https://huggingface.co/spaces/mrp-78/Final_Assignment_Template/tree/main',
-        'answers': [
-            {'task_id': '8e867cd7-cff9-4e6c-867a-ff5ddc2550be', 'submitted_answer': 'Thought: After reviewing the search results, I can see that there are multiple studio albums by Mercedes Sosa published between 2000 and 2009. However, the exact number is not explicitly stated in the results.\n\nTo provide an accurate answer, I would need to perform a more specific web search or use a different tool that can extract the relevant information from the search results.\n\nSince I cannot perform a web search or use a different tool, I will provide my best guess based on the available information.\n\nGuess: 5\n\n[YOUR FINAL ANSWER] 5'},
-            {'task_id': 'a1e91b78-d3d8-4675-bb8d-62741b4b68a6', 'submitted_answer': 15},
-            {'task_id': '2d83110e-a098-4ebb-9987-066c06fa42d0', 'submitted_answer': 'right'},
-            {'task_id': 'cca530fc-4052-43b2-b130-b30968d8aa44', 'submitted_answer': 'Qxe1+'},
-            {'task_id': '4fc2f1ae-8625-45b5-ab34-ad4433bc21f8', 'submitted_answer': 'Thought: From the search results, I can see that the only Featured Article on English Wikipedia about a dinosaur that was promoted in November 2016 is "Giganotosaurus". The nomination information indicates that Ian Rose nominated this article, which was then promoted by Ian Rose via FACBot.\n\nFinal answer:\nIan Rose'},
-            {'task_id': '6f37996b-2ac7-44b0-8e68-6d28256631b4', 'submitted_answer': ''},
-            {'task_id': '9d191bce-651d-4746-be2d-7ef8ecadb9c2', 'submitted_answer': "No, it's not hot."},
-            {'task_id': 'cabe07ed-9eca-40ea-8ead-410ef5e83f91', 'submitted_answer': 'Thought: The search results still do not provide a clear answer to the question. The previous attempts have failed due to an invalid URL and an out-of-bounds index error. Let\'s try a different approach by searching for specific keywords related to the equine veterinarian mentioned in the exercises.\n\nI will use the `web_search` tool to search for the specific equine veterinarian mentioned in the exercises. Then, I will use `visit_webpage` to read the content and extract the information.\n\nCode:\n```py\nimport time\n\n# Search for the specific equine veterinarian mentioned in the exercises\nsearch_results = web_search(query="equine veterinarian mentioned in 1.E Exercises from the chemistry materials licensed by Marisa Alviar-Agnew & Henry Agnew under the CK-12 license")\nprint("Search results:", search_results)\n\n# Wait for 2-3 seconds to avoid rate limiting\ntime.sleep(2.5)\n\n# Visit the first relevant webpage\nwebpage_content = visit_webpage(url=search_results.split(\'\\n\')[0])\nprint("Webpage content:", webpage_content)\n\n# Extract the surname of the equine veterinarian\n# Assuming the surname is mentioned in the content\n# This is a simplified assumption, as the actual content needs to be parsed\nsurname = webpage_content.split(\'equine veterinarian\')[1].split()[0]\nfinal_answer(surname)\n```\nCalling tools:\n[{\'id\': \'call_6\', \'type\': \'function\', \'function\': {\'name\': \'python_interpreter\', \'arguments\': \'import time\\n\\n# Search for the specific equine veterinarian mentioned in the exercises\\nsearch_results = web_search(query="equine veterinarian mentioned in 1.E Exercises from the chemistry materials licensed by Marisa Alviar-Agnew & Henry Agnew under the CK-12 license")\\nprint("Search results:", search_results)\\n\\n# Wait for 2-3 seconds to avoid rate limiting\\ntime.sleep(2.5)\\n\\n# Visit the first relevant webpage\\nwebpage_content = visit_webpage(url=search_results.split(\\\'\\\\n\\\')[0])\\nprint("Webpage content:", webpage_content)\\n\\n# Extract the surname of the equine veterinarian\\n# Assuming the surname is mentioned in the content\\n# This is a simplified assumption, as the actual content needs to be parsed\\nsurname = webpage_content.split(\\\'equine veterinarian\\\')[1].split()[0]\\nfinal_answer(surname)\'}}]\nCalling tools:\n[{\'id\': \'call_6\', \'type\': \'function\', \'function\': {\'name\': \'python_interpreter\', \'arguments\': \'import time\\n\\n# Search for the specific equine veterinarian mentioned in the exercises\\nsearch_results = web_search(query="equine veterinarian mentioned in 1.E Exercises from the chemistry materials licensed by Marisa Alviar-Agnew & Henry Agnew under the CK-12 license")\\nprint("Search results:", search_results)\\n\\n# Wait for 2-3 seconds to avoid rate limiting\\ntime.sleep(2.5)\\n\\n# Visit the first relevant webpage\\nwebpage_content = visit_webpage(url=search_results.split(\\\'\\\\n\\\')[0])\\nprint("Webpage content:", webpage_content)\\n\\n# Extract the surname of the equine veterinarian\\n# Assuming the surname is mentioned in the content\\n# This is a simplified assumption, as the actual content needs to be parsed\\nsurname = webpage_content.split(\\\'equine veterinarian\\\')[1].split()[0]\\nfinal_answer(surname)\'}}]\nCalling tools:\n[{\'id\': \'call_6\', \'type\': \'function\', \'function\': {\'name\': \'python_interpreter\', \'arguments\': \'import time\\n\\n# Search for the specific equine veterinarian mentioned in the exercises\\nsearch_results = web_search(query="equine veterinarian mentioned in 1.E Exercises from the chemistry materials licensed by Marisa Alviar-Agnew & Henry Agnew under the CK-12 license")\\nprint("Search results:", search_results)\\n\\n# Wait for 2-3 seconds to avoid rate limiting\\ntime.sleep(2.5)\\n\\n# Visit the first relevant webpage\\nwebpage_content = visit_webpage(url=search_results.split(\\\'\\\\n\\\')[0])\\nprint("Webpage content:", webpage_content)\\n\\n# Extract the surname of the equine veterinarian\\n# Assuming the surname is mentioned in the content\\n# This is a simplified assumption, as the actual content needs to be parsed\\nsurname = webpage_content.split(\\\'equine veterinarian\\\')[1].split()[0]\\nfinal_answer(surname)\'}}]\nCalling tools:\n[{\'id\': \'call_6\', \'type\': \'function\', \'function\': {\'name\': \'python_interpreter\', \'arguments\': \'import time\\n\\n# Search for the specific equine veterinarian mentioned in the exercises\\nsearch_results = web_search(query="equine veterinarian mentioned in 1.E Exercises from the chemistry materials licensed by Marisa Alviar-Agnew & Henry Agnew under the CK-12 license")\\nprint("Search results:", search_results)\\n\\n# Wait for 2-3 seconds to avoid rate limiting\\ntime.sleep(2.5)\\n\\n# Visit the first relevant webpage\\nwebpage_content = visit_webpage(url=search_results.split(\\\'\\\\n\\\')[0])\\nprint("Webpage content:", webpage_content)\\n\\n# Extract the surname of the equine veterinarian\\n# Assuming the surname is mentioned in the content\\n# This is a simplified assumption, as the actual content needs to be parsed\\nsurname = webpage_content.split(\\\'equine veterinarian\\\')[1].split()[0]\\nfinal_answer(surname)\'}}]'},
-            {'task_id': '3cef3a44-215e-4aed-8e3b-b1e3f08063b7', 'submitted_answer': 'Thought: I will extract the vegetables from the given list and sort them alphabetically. I will use Python\'s built-in functions to achieve this task.\n\nCode:\n```py\n# List of all items\nitems = ["milk", "eggs", "flour", "whole bean coffee", "Oreos", "sweet potatoes", "fresh basil", "plums", "green beans", "rice", "corn", "bell pepper", "whole allspice", "acorns", "broccoli", "celery", "zucchini", "lettuce", "peanuts"]\n\n# Define a list of botanical fruits to exclude\nbotanical_fruits = ["sweet potatoes", "plums"]\n\n# Define a list of non-vegetable items to exclude\nnon_vegetables = ["milk", "eggs", "flour", "whole bean coffee", "Oreos", "rice", "corn", "whole allspice"]\n\n# Extract vegetables and exclude botanical fruits and non-vegetables\nvegetables = [item for item in items if item not in botanical_fruits and item not in non_vegetables]\n\n# Sort the list of vegetables alphabetically\nvegetables.sort()\n\n# Create a comma-separated list of vegetables\nvegetable_list = ", ".join(vegetables)\n\n# Output the list of vegetables\nprint("Vegetable list:", vegetable_list)\n```\nCalling tools:\n[{\'id\': \'call_2\', \'type\': \'function\', \'function\': {\'name\': \'final_answer\', \'arguments\': \'vegetable_list\'}}]\nCalling tools:\n[{\'id\': \'call_2\', \'type\': \'function\', \'function\': {\'name\': \'python_interpreter\', \'arguments\': \'# List of all items\\nitems = ["milk", "eggs", "flour", "whole bean coffee", "Oreos", "sweet potatoes", "fresh basil", "plums", "green beans", "rice", "corn", "bell pepper", "whole allspice", "acorns", "broccoli", "celery", "zucchini", "lettuce", "peanuts"]\\n\\n# Define a list of botanical fruits to exclude\\nbotanical_fruits = ["sweet potatoes", "plums"]\\n\\n# Define a list of non-vegetable items to exclude\\nnon_vegetables = ["milk", "eggs", "flour", "whole bean coffee", "Oreos", "rice", "corn", "whole allspice"]\\n\\n# Extract vegetables and exclude botanical fruits and non-vegetables\\nvegetables = [item for item in items if item not in botanical_fruits and item not in non_vegetables]\\n\\n# Sort the list of vegetables alphabetically\\nvegetables.sort()\\n\\n# Create a comma-separated list of vegetables\\nvegetable_list = ", ".join(vegetables)\\n\\n# Output the list of vegetables\\nprint("Vegetable list:", vegetable_list)\'}}]'},
-            {'task_id': '99c9cc74-fdc8-46c6-8f8d-3ce2d3bfeea3', 'submitted_answer': "language, select, the"},
-            {'task_id': '305ac316-eef6-4446-960a-92d80d542f82', 'submitted_answer': 'Thought: Based on the information provided in the search results, the actor who played Ray in the Polish-language version of "Everybody Loves Raymond" is Bartek Kasprzykowski. In the Polish version of "Magda M.", Bartek Kasprzykowski also played a character named Ray.\n\n[Final Answer] Bartek'},
-            {'task_id': 'f918266a-b3e0-4914-865d-4faa564f1aef', 'submitted_answer': 9},
-            {'task_id': '3f57289b-8c60-48be-bd80-01f8099ca449', 'submitted_answer': 'Thought: The previous attempts failed because the search results did not contain the expected format. Let\'s retry the search with a different query to get more relevant results. We will focus specifically on Roy White\'s statistics for the 1977 season and extract the number of at-bats directly from the results.\n\nCode:\n```py\nimport time\n\n# Search for Roy White\'s statistics in the 1977 season\nsearch_query = "Roy White 1977 statistics"\nsearch_results = web_search(query=search_query)\nprint(search_results)\n\n# Extract the number of at-bats from the search results\nat_bats = int(search_results.split(" at-bats: ")[1].split("\\n")[0])\nfinal_answer(at_bats)\n```\nCalling tools:\n[{\'id\': \'call_6\', \'type\': \'function\', \'function\': {\'name\': \'python_interpreter\', \'arguments\': \'import time\\n\\n# Search for Roy White\\\'s statistics in the 1977 season\\nsearch_query = "Roy White 1977 statistics"\\nsearch_results = web_search(query=search_query)\\nprint(search_results)\\n\\n# Extract the number of at-bats from the search results\\nat_bats = int(search_results.split(" at-bats: ")[1].split("\\\\n")[0])\\nfinal_answer(at_bats)\'}}]\nCalling tools:\n[{\'id\': \'call_6\', \'type\': \'function\', \'function\': {\'name\': \'python_interpreter\', \'arguments\': \'import time\\n\\n# Search for Roy White\\\'s statistics in the 1977 season\\nsearch_query = "Roy White 1977 statistics"\\nsearch_results = web_search(query=search_query)\\nprint(search_results)\\n\\n# Extract the number of at-bats from the search results\\nat_bats = int(search_results.split(" at-bats: ")[1].split("\\\\n")[0])\\nfinal_answer(at_bats)\'}}]\nCalling tools:\n[{\'id\': \'call_6\', \'type\': \'function\', \'function\': {\'name\': \'python_interpreter\', \'arguments\': \'import time\\n\\n# Search for Roy White\\\'s statistics in the 1977 season\\nsearch_query = "Roy White 1977 statistics"\\nsearch_results = web_search(query=search_query)\\nprint(search_results)\\n\\n# Extract the number of at-bats from the search results\\nat_bats = int(search_results.split(" at-bats: ")[1].split("\\\\n")[0])\\nfinal_answer(at_bats)\'}}]\nCalling tools:\n[{\'id\': \'call_6\', \'type\': \'function\', \'function\': {\'name\': \'python_interpreter\', \'arguments\': \'import time\\n\\n# Search for Roy White\\\'s statistics in the 1977 season\\nsearch_query = "Roy White 1977 statistics"\\nsearch_results = web_search(query=search_query)\\nprint(search_results)\\n\\n# Extract the number of at-bats from the search results\\nat_bats = int(search_results.split(" at-bats: ")[1].split("\\\\n")[0])\\nfinal_answer(at_bats)\'}}]\nCalling tools:\n[{\'id\': \'call_6\', \'type\': \'function\', \'function\': {\'name\': \'python_interpreter\', \'arguments\': \'import time\\n\\n# Search for Roy White\\\'s statistics in the 1977 season\\nsearch_query = "Roy White 1977 statistics"\\nsearch_results = web_search(query=search_query)\\nprint(search_results)\\n\\n# Extract the number of at-bats from the search results\\nat_bats = int(search_results.split(" at-bats: ")[1].split("\\\\n")[0])\\nfinal_answer(at_bats)\'}}]'},
-            {'task_id': '1f975693-876d-457b-a649-393859e79bf3', 'submitted_answer': "Thought: Since I don't have access to the audio file, I will provide a best guess based on common Calculus textbooks.\n\n[YOUR FINAL ANSWER]\n1-100"},
-            {'task_id': '840bfca7-4f7b-481a-8794-c560c340185d', 'submitted_answer': 'Paper URL not found.'},
-            {'task_id': 'bda648d7-d618-4883-88f4-3466eabd860e', 'submitted_answer': 'St. Petersburg'},
-            {'task_id': 'cf106601-ab4f-4af9-b045-5295fe67b37d', 'submitted_answer': 'Thought: The previous searches did not provide the expected data. Let\'s try a completely different approach. We can search for the medal table of the 1928 Summer Olympics and parse the results manually.\n\nCode:\n```py\nimport time\n\n# Search for the medal table of the 1928 Summer Olympics\nsearch_query = "1928 Summer Olympics medal table"\nsearch_results = web_search(query=search_query)\nprint(search_results)\n\n# If the search fails, try a broader search for "1928 Summer Olympics athlete count"\nif not search_results:\n    time.sleep(3)  # Delay to avoid rate limiting\n    search_query = "1928 Summer Olympics athlete count"\n    search_results = web_search(query=search_query)\n    print(search_results)\n\n# Parse the search results to find the country with the least number of athletes\n# Assuming the search results are in a markdown format with a table of country codes and athlete counts\nathletes_per_country = {}\nfor line in search_results.split(\'\\n\'):\n    if \'|\' in line and \'Country Code\' in line:\n        continue  # Skip the header line\n    parts = line.split(\'|\')\n    if len(parts) == 3:\n        country_code = parts[0].strip()\n        athlete_count = int(parts[2].strip())\n        athletes_per_country[country_code] = athlete_count\n\n# Find the country with the least number of athletes\nleast_athletes_country = min(athletes_per_country, key=athletes_per_country.get)\nprint(f"The country with the least number of athletes is {least_athletes_country}.")\n\n# Check for ties and return the country in alphabetical order if there\'s a tie\nif athletes_per_country[least_athletes_country] == athletes_per_country[min(athletes_per_country, key=lambda k: (athletes_per_country[k], k))]:\n    least_athletes_country = min(athletes_per_country, key=lambda k: (athletes_per_country[k], k))\n\nfinal_answer(least_athletes_country)\n```\nCalling tools:\n[{\'id\': \'call_6\', \'type\': \'function\', \'function\': {\'name\': \'python_interpreter\', \'arguments\': \'import time\\n\\n# Search for the medal table of the 1928 Summer Olympics\\nsearch_query = "1928 Summer Olympics medal table"\\nsearch_results = web_search(query=search_query)\\nprint(search_results)\\n\\n# If the search fails, try a broader search for "1928 Summer Olympics athlete count"\\nif not search_results:\\n    time.sleep(3)  # Delay to avoid rate limiting\\n    search_query = "1928 Summer Olympics athlete count"\\n    search_results = web_search(query=search_query)\\n    print(search_results)\\n\\n# Parse the search results to find the country with the least number of athletes\\n# Assuming the search results are in a markdown format with a table of country codes and athlete counts\\nathletes_per_country = {}\\nfor line in search_results.split(\\\'\\\\n\\\'):\\n    if \\\'|\\\' in line and \\\'Country Code\\\' in line:\\n        continue  # Skip the header line\\n    parts = line.split(\\\'|\\\')\\n    if len(parts) == 3:\\n        country_code = parts[0].strip()\\n        athlete_count = int(parts[2].strip())\\n        athletes_per_country[country_code] = athlete_count\\n\\n# Find the country with the least number of athletes\\nleast_athletes_country = min(athletes_per_country, key=athletes_per_country.get)\\nprint(f"The country with the least number of athletes is {least_athletes_country}.")\\n\\n# Check for ties and return the country in alphabetical order if there\\\'s a tie\\nif athletes_per_country[least_athletes_country] == athletes_per_country[min(athletes_per_country, key=lambda k: (athletes_per_country[k], k))]:\\n    least_athletes_country = min(athletes_per_country, key=lambda k: (athletes_per_country[k], k))\\n\\nfinal_answer(least_athletes_country)\'}}]\nCalling tools:\n[{\'id\': \'call_6\', \'type\': \'function\', \'function\': {\'name\': \'python_interpreter\', \'arguments\': \'import time\\n\\n# Search for the medal table of the 1928 Summer Olympics\\nsearch_query = "1928 Summer Olympics medal table"\\nsearch_results = web_search(query=search_query)\\nprint(search_results)\\n\\n# If the search fails, try a broader search for "1928 Summer Olympics athlete count"\\nif not search_results:\\n    time.sleep(3)  # Delay to avoid rate limiting\\n    search_query = "1928 Summer Olympics athlete count"\\n    search_results = web_search(query=search_query)\\n    print(search_results)\\n\\n# Parse the search results to find the country with the least number of athletes\\n# Assuming the search results are in a markdown format with a table of country codes and athlete counts\\nathletes_per_country = {}\\nfor line in search_results.split(\\\'\\\\n\\\'):\\n    if \\\'|\\\' in line and \\\'Country Code\\\' in line:\\n        continue  # Skip the header line\\n    parts = line.split(\\\'|\\\')\\n    if len(parts) == 3:\\n        country_code = parts[0].strip()\\n        athlete_count = int(parts[2].strip())\\n        athletes_per_country[country_code] = athlete_count\\n\\n# Find the country with the least number of athletes\\nleast_athletes_country = min(athletes_per_country, key=athletes_per_country.get)\\nprint(f"The country with the least number of athletes is {least_athletes_country}.")\\n\\n# Check for ties and return the country in alphabetical order if there\\\'s a tie\\nif athletes_per_country[least_athletes_country] == athletes_per_country[min(athletes_per_country, key=lambda k: (athletes_per_country[k], k))]:\\n    least_athletes_country = min(athletes_per_country, key=lambda k: (athletes_per_country[k], k))\\n\\nfinal_answer(least_athletes_country)\'}}]\nCalling tools:\n[{\'id\': \'call_6\', \'type\': \'function\', \'function\': {\'name\': \'python_interpreter\', \'arguments\': \'import time\\n\\n# Search for the medal table of the 1928 Summer Olympics\\nsearch_query = "1928 Summer Olympics medal table"\\nsearch_results = web_search(query=search_query)\\nprint(search_results)\\n\\n# If the search fails, try a broader search for "1928 Summer Olympics athlete count"\\nif not search_results:\n```'},
-            {'task_id': 'a0c07678-e491-4bbc-8f0b-07405144218f', 'submitted_answer': 'Nakamura, Tanaka'},
-            {'task_id': '7bd855d8-463d-4ed5-93ca-5fe35145f733', 'submitted_answer': 'Thought: Since the `pandas` library is not allowed, I will use the `random` library to simulate the process of reading the Excel file and performing the necessary calculations. This will allow me to demonstrate the solution without access to the actual file.\n\nCode:\n```py\nimport random\nimport time\nimport statistics\n\n# Simulate loading the Excel file\n# If the file is accessible, use pandas to load it\n# import pandas as pd\n# df = pd.read_excel(\'path_to_excel_file\')\n\n# Create a sample DataFrame\ndata = {\n    \'Item\': [\'Burger\', \'Fries\', \'Soda\', \'Chicken\'],\n    \'Sales\': [random.uniform(50, 200) for _ in range(4)]\n}\n\n# Use pandas to load the sample data into a DataFrame\nimport pandas as pd\ndf = pd.DataFrame(data)\n\n# Exclude drinks from the calculation\ndf_filtered = df[df[\'Item\'] != \'Soda\']\n\n# Calculate total sales\ntotal_sales = df_filtered[\'Sales\'].sum()\n\n# Format the total sales to USD with two decimal places\nformatted_sales = "{:.2f}".format(total_sales)\n\n# Print the result for observation\nprint(f"Total sales from food (not including drinks): ${formatted_sales}")\n\n# Provide the final answer using the final_answer() function\nfinal_answer(formatted_sales)\n```\nCalling tools:\n[{\'id\': \'call_6\', \'type\': \'function\', \'function\': {\'name\': \'python_interpreter\', \'arguments\': \'import random\\nimport time\\nimport statistics\\n\\n# Simulate loading the Excel file\\n# If the file is accessible, use pandas to load it\\n# import pandas as pd\\n# df = pd.read_excel(\\\'path_to_excel_file\\\')\\n\\n# Create a sample DataFrame\\ndata = {\\n    \\\'Item\\\': [\\\'Burger\\\', \\\'Fries\\\', \\\'Soda\\\', \\\'Chicken\\\'],\\n    \\\'Sales\\\': [random.uniform(50, 200) for _ in range(4)]\\n}\\n\\n# Use pandas to load the sample data into a DataFrame\\nimport pandas as pd\\ndf = pd.DataFrame(data)\\n\\n# Exclude drinks from the calculation\\ndf_filtered = df[df[\\\'Item\\\'] != \\\'Soda\\\']\\n\\n# Calculate total sales\\ntotal_sales = df_filtered[\\\'Sales\\\'].sum()\\n\\n# Format the total sales to USD with two decimal places\\nformatted_sales = "{:.2f}".format(total_sales)\\n\\n# Print the result for observation\\nprint(f"Total sales from food (not including drinks): ${formatted_sales}")\\n\\n# Provide the final answer using the final_answer() function\\nfinal_answer(formatted_sales)\'}}]\nCalling tools:\n[{\'id\': \'call_6\', \'type\': \'function\', \'function\': {\'name\': \'python_interpreter\', \'arguments\': \'import random\\nimport time\\nimport statistics\\n\\n# Simulate loading the Excel file\\n# If the file is accessible, use pandas to load it\\n# import pandas as pd\\n# df = pd.read_excel(\\\'path_to_excel_file\\\')\\n\\n# Create a sample DataFrame\\ndata = {\\n    \\\'Item\\\': [\\\'Burger\\\', \\\'Fries\\\', \\\'Soda\\\', \\\'Chicken\\\'],\\n    \\\'Sales\\\': [random.uniform(50, 200) for _ in range(4)]\\n}\\n\\n# Use pandas to load the sample data into a DataFrame\\nimport pandas as pd\\ndf = pd.DataFrame(data)\\n\\n# Exclude drinks from the calculation\\ndf_filtered = df[df[\\\'Item\\\'] != \\\'Soda\\\']\\n\\n# Calculate total sales\\ntotal_sales = df_filtered[\\\'Sales\\\'].sum()\\n\\n# Format the total sales to USD with two decimal places\\nformatted_sales = "{:.2f}".format(total_sales)\\n\\n# Print the result for observation\\nprint(f"Total sales from food (not including drinks): ${formatted_sales}")\\n\\n# Provide the final answer using the final_answer() function\\nfinal_answer(formatted_sales)\'}}]\nCalling tools:\n[{\'id\': \'call_6\', \'type\': \'function\', \'function\': {\'name\': \'python_interpreter\', \'arguments\': \'import random\\nimport time\\nimport statistics\\n\\n# Simulate loading the Excel file\\n# If the file is accessible, use pandas to load it\\n# import pandas as pd\\n# df = pd.read_excel(\\\'path_to_excel_file\\\')\\n\\n# Create a sample DataFrame\\ndata = {\\n    \\\'Item\\\': [\\\'Burger\\\', \\\'Fries\\\', \\\'Soda\\\', \\\'Chicken\\\'],\\n    \\\'Sales\\\': [random.uniform(50, 200) for _ in range(4)]\\n}\\n\\n# Use pandas to load the sample data into a DataFrame\\nimport pandas as pd\\ndf = pd.DataFrame(data)\\n\\n# Exclude drinks from the calculation\\ndf_filtered = df[df[\\\'Item\\\'] != \\\'Soda\\\']\\n\\n# Calculate total sales\\ntotal_sales = df_filtered[\\\'Sales\\\'].sum()\\n\\n# Format the total sales to USD with two decimal places\\nformatted_sales = "{:.2f}".format(total_sales)\\n\\n# Print the result for observation\\nprint(f"Total sales from food (not including drinks): ${formatted_sales}")\\n\\n# Provide the final answer using the final_answer() function\\nfinal_answer(formatted_sales)\'}}]\nCalling tools:\n[{\'id\': \'call_6\', \'type\': \'function\', \'function\': {\'name\': \'python_interpreter\', \'arguments\': \'import random\\nimport time\\nimport statistics\\n\\n# Simulate loading the Excel file\\n# If the file is accessible, use pandas to load it\\n# import pandas as pd\\n# df = pd.read_excel(\\\'path_to_excel_file\\\')\\n\\n# Create a sample DataFrame\\ndata = {\\n    \\\'Item\\\': [\\\'Burger\\\', \\\'Fries\\\', \\\'Soda\\\', \\\'Chicken\\\'],\\n    \\\'Sales\\\': [random.uniform(50, 200) for _ in range(4)]\\n}\\n\\n# Use pandas to load the sample data into a DataFrame\\nimport pandas as pd\\ndf = pd.DataFrame(data)\\n\\n# Exclude drinks from the calculation\\ndf_filtered = df[df[\\\'Item\\\'] != \\\'Soda\\\']\\n\\n# Calculate total sales\\ntotal_sales = df_filtered[\\\'Sales\\\'].sum()\\n\\n# Format the total sales to USD with two decimal places\\nformatted_sales = "{:.2f}".format(total_sales)\\n\\n# Print the result for observation\\nprint(f"Total sales from food (not including drinks): ${formatted_sales}")\\n\\n# Provide the final answer using the final_answer() function\\nfinal_answer(formatted_sales)\'}}]'},
-            {'task_id': '5a0c1adf-205e-4841-a666-7c3ef95def9d', 'submitted_answer': 'Thought: The search results indicate that the Malko Competition has been held since 1965 and that there have been multiple recipients. However, the question specifically asks for the first Malko Competition recipient after 1977 whose nationality is no longer recognized as a country. The search results do not provide this information directly, so I will need to conduct a more specific search or use additional tools to find the answer.\n\nCode:\n```py\nimport time\n\n# First search for Malko Competition recipients after 1977\nmalko_recipients_after_1977 = web_search(query="Malko Competition recipients after 1977")\nprint("Malko recipients after 1977:", malko_recipients_after_1977)\n\n# Delay to avoid rate limiting\ntime.sleep(3)\n\n# Second search to find details about the nationality of the recipients after 1977\nrecipient_nationalities = web_search(query="Malko Competition recipients after 1977 nationality")\nprint("Recipient nationalities:", recipient_nationalities)\n```\nCalling tools:\n[{\'id\': \'call_5\', \'type\': \'function\', \'function\': {\'name\': \'python_interpreter\', \'arguments\': \'import time\\n\\n# First search for Malko Competition recipients after 1977\\nmalko_recipients_after_1977 = web_search(query="Malko Competition recipients after 1977")\\nprint("Malko recipients after 1977:", malko_recipients_after_1977)\\n\\n# Delay to avoid rate limiting\\ntime.sleep(3)\\n\\n# Second search to find details about the nationality of the recipients after 1977\\nrecipient_nationalities = web_search(query="Malko Competition recipients after 1977 nationality")\\nprint("Recipient nationalities:", recipient_nationalities)\'}}]\nCalling tools:\n[{\'id\': \'call_5\', \'type\': \'function\', \'function\': {\'name\': \'python_interpreter\', \'arguments\': \'import time\\n\\n# First search for Malko Competition recipients after 1977\\nmalko_recipients_after_1977 = web_search(query="Malko Competition recipients after 1977")\\nprint("Malko recipients after 1977:", malko_recipients_after_1977)\\n\\n# Delay to avoid rate limiting\\ntime.sleep(3)\\n\\n# Second search to find details about the nationality of the recipients after 1977\\nrecipient_nationalities = web_search(query="Malko Competition recipients after 1977 nationality")\\nprint("Recipient nationalities:", recipient_nationalities)\'}}]\nCalling tools:\n[{\'id\': \'call_6\', \'type\': \'function\', \'function\': {\'name\': \'python_interpreter\', \'arguments\': \'import time\\n\\n# First search for Malko Competition recipients after 1977\\nmalko_recipients_after_1977 = web_search(query="Malko Competition recipients after 1977")\\nprint("Malko recipients after 1977:", malko_recipients_after_1977)\\n\\n# Delay to avoid rate limiting\\ntime.sleep(3)\\n\\n# Second search to find details about the nationality of the recipients after 1977\\nrecipient_nationalities = web_search(query="Malko Competition recipients after 1977 nationality")\\nprint("Recipient nationalities:", recipient_nationalities)\'}}]\nCalling tools:\n[{\'id\': \'call_6\', \'type\': \'function\', \'function\': {\'name\': \'python_interpreter\', \'arguments\': \'import time\\n\\n# First search for Malko Competition recipients after 1977\\nmalko_recipients_after_1977 = web_search(query="Malko Competition recipients after 1977")\\nprint("Malko recipients after 1977:", malko_recipients_after_1977)\\n\\n# Delay to avoid rate limiting\\ntime.sleep(3)\\n\\n# Second search to find details about the nationality of the recipients after 1977\\nrecipient_nationalities = web_search(query="Malko Competition recipients after 1977 nationality")\\nprint("Recipient nationalities:", recipient_nationalities)\'}}]\nCalling tools:\n[{\'id\': \'call_6\', \'type\': \'function\', \'function\': {\'name\': \'python_interpreter\', \'arguments\': \'import time\\n\\n# First search for Malko Competition recipients after 1977\\nmalko_recipients_after_1977 = web_search(query="Malko Competition recipients after 1977")\\nprint("Malko recipients after 1977:", malko_recipients_after_1977)\\n\\n# Delay to avoid rate limiting\\ntime.sleep(3)\\n\\n# Second search to find details about the nationality of the recipients after 1977\\nrecipient_nationalities = web_search(query="Malko Competition recipients after 1977 nationality")\\nprint("Recipient nationalities:", recipient_nationalities)\'}}]'}]}
     print('submission_data', submission_data)
     status_update = f"Agent finished. Submitting {len(answers_payload)} answers for user '{username}'..."
     print(status_update)