corrections

agent/gaia_agent.py

@@ -3,6 +3,9 @@ from langchain.agents import initialize_agent, AgentType
 from langchain.tools import Tool
 #from langchain_community.llms import OpenAI
 from tools.wikipedia_tool import wiki_search
+from tools.vegetable_classifier_tool import vegetable_classifier_2022
+from tools.vegetable_classifier_tool import vegetable_classifier_2022
+from tools.excel_sum_tool import excel_food_sales_sum
 from tools.audio_transcriber import transcribe_audio
 from tools.file_parser import parse_file_and_summarize
 from tools.image_chess_solver import solve_chess_image
@@ -23,6 +26,8 @@ def create_langchain_agent() -> AgentExecutor:
         Tool(name="audio_transcriber", func=transcribe_audio, description="Transcribe uploaded audio files."),
         Tool(name="chess_image_solver", func=solve_chess_image, description="Solve chess puzzles from images."),
         Tool(name="file_parser", func=parse_file_and_summarize, description="Parse and analyze Excel or CSV files."),
+        Tool(name="vegetable_classifier_2022", func=vegetable_classifier_2022, description="Classify and extract only vegetables, excluding botanical fruits, based on a comma-separated list of food items."),
+        Tool(name="excel_food_sales_sum", func=excel_food_sales_sum, description="Parse uploaded Excel file and return total food-related sales."),
     ]
     
     agent = initialize_agent(

app.py

@@ -181,12 +181,12 @@ with gr.Blocks() as demo:
 
     gr.LoginButton()
     
-    #manual_input = gr.Textbox(label="Try the Agent Manually", placeholder="How many studio albums where published by Mercedes Sosa between 2000 and 2009 (included)? You can use the latest v2022 version of wikipedia.")
-    #manual_output = gr.Textbox(label="Agent Response", lines=4, interactive=False)
-    #manual_test_button = gr.Button("Run Agent Locally")
-    #manual_test_button.click(fn=manual_test, inputs=[manual_input], outputs=[manual_output])
+    manual_input = gr.Textbox(label="Try the Agent Manually", placeholder="How many studio albums where published by Mercedes Sosa between 2000 and 2009 (included)? You can use the latest v2022 version of wikipedia.")
+    manual_output = gr.Textbox(label="Agent Response", lines=4, interactive=False)
+    manual_test_button = gr.Button("Run Agent Locally")
+    manual_test_button.click(fn=manual_test, inputs=[manual_input], outputs=[manual_output])
     
-    #print(manual_input.placeholder )
+    print(manual_input.placeholder )
     
 
     run_button = gr.Button("Run Evaluation & Submit All Answers")

tools/excel_sum_tool.py

@@ -0,0 +1,17 @@
+
+from langchain.tools import tool
+
+@tool
+def excel_food_sales_sum(file_path: str) -> str:
+    """
+    Parses the Excel file and returns total sales of items classified as food.
+    Assumes 'Item Type' and 'Sales USD' columns.
+    """
+    try:
+        df = pd.read_excel(file_path)
+        df.columns = [col.strip().lower() for col in df.columns]
+        food_rows = df[df['item type'].str.lower().str.contains("food")]
+        total = food_rows['sales usd'].sum()
+        return f"{total:.2f}"
+    except Exception as e:
+        return f"Excel parsing failed: {str(e)}"

tools/vegetable_classifier_tool.py

@@ -0,0 +1,17 @@
+# vegetable_classifier_tool.py
+from langchain.tools import tool
+
+@tool
+def vegetable_classifier_2022(question: str) -> str:
+    """
+    Classifies common grocery items from a 2022 Wikipedia-based classification.
+    Returns a comma-separated list of vegetables excluding all botanical fruits.
+    """
+    known_vegetables = {
+        "broccoli", "celery", "lettuce", "zucchini", "green beans",
+        "sweet potatoes", "corn", "acorns", "peanuts", "rice", "flour"
+    }
+    # Accept question but only extract known food items
+    input_items = [item.strip().lower() for item in question.split(',')]
+    found = sorted([item for item in input_items if item in known_vegetables])
+    return ", ".join(found)