Update app.py

app.py

@@ -15,105 +15,66 @@ openai_key = os.environ.get("OPENAI_API_KEY")
 
 search_tool = DuckDuckGoSearchTool()
 
-##Tool 2 
-from smolagents import Tool
-from huggingface_hub import hf_hub_download
-import pandas as pd
-
-class ExcelAnalysisTool(Tool):
-    name = "excel_analysis"
-    description = (
-        "Loads an Excel file from the GAIA dataset on Hugging Face and calculates "
-        "the total sales for items labeled as 'food', excluding drinks. "
-        "Provide input as a string with the filename, e.g., 'sales_data.xlsx'."
-    )
-
-    inputs = {
-        "filename": {
-            "type": "string",
-            "description": "The name of the Excel file (e.g., 'sales_data.xlsx')"
-        }
-    }
-
-    output_type = "string"
-    repo_id = "gaia-benchmark/GAIA"
-
-    def forward(self, filename: str) -> str:
-        try:
-            file_path = hf_hub_download(
-                repo_id=self.repo_id,
-                filename=filename,
-                repo_type="dataset"
-            )
-            df = pd.read_excel(file_path)
-            food_sales = df[
-                (df['category'].str.lower() == 'food') &
-                (df['item'].str.lower() != 'drinks')
-            ]
-            total_sales = food_sales['sales'].sum()
-            return f"Total sales for food items: ${total_sales:.2f}"
-        except FileNotFoundError:
-            return "Error: The specified file was not found."
-        except KeyError as e:
-            return f"Error: Missing expected column in the Excel file: {str(e)}"
-        except Exception as e:
-            return f"An unexpected error occurred: {str(e)}"
 
-##Tool 3
+##Tool 2
 import wikipedia
 from smolagents import Tool
+from smolagents.models import InferenceClientModel
 
-class WikiTool(Tool):
-    name = "wiki_tool"
+class WikipediaQATool(Tool):
+    name = "wikipedia_qa"
     description = (
-        "Performs Wikipedia lookups. Actions supported: 'summary' and 'is_historical_country'."
+        "Searches Wikipedia for a topic, reads its content, and answers the input question "
+        "based on the content of the Wikipedia page."
     )
 
     inputs = {
-        "action": {
+        "question": {
             "type": "string",
-            "description": "The action to perform: 'summary' or 'is_historical_country'"
+            "description": "The question that should be answered using Wikipedia."
         },
         "topic": {
             "type": "string",
-            "description": "The topic or country name to look up"
+            "description": "The topic to search for on Wikipedia."
         }
     }
 
     output_type = "string"
 
-    def forward(self, action: str, topic: str) -> str:
-        if action == "summary":
-            return self.fetch_summary(topic)
-        elif action == "is_historical_country":
-            return self.is_historical_country(topic)
-        else:
-            return "Error: Unknown action. Use 'summary' or 'is_historical_country'."
+    def __init__(self, model=None):
+        super().__init__()
+        self.model = model or InferenceClientModel(
+            model="mistralai/Magistral-Small-2506", provider="featherless-ai"
+        )
 
-    def fetch_summary(self, topic: str) -> str:
+    def forward(self, question: str, topic: str) -> str:
         try:
-            return wikipedia.summary(topic, sentences=3)
+            page = wikipedia.page(topic)
+            content = page.content[:2000]  # Limit for context
+
+            # Build QA prompt
+            prompt = (
+                f"You are a Wikipedia expert. Based only on the following content from the Wikipedia page on '{topic}', "
+                f"answer the question briefly and factually.\n\n"
+                f"=== Wikipedia Content ===\n{content}\n\n"
+                f"=== Question ===\n{question}\n\n"
+                f"Answer in a single line. Avoid any extra explanation.\n"
+                f"FINAL ANSWER:"
+            )
+
+            response = self.model(prompt)
+            return response.strip()
+
         except wikipedia.DisambiguationError as e:
-            return f"Disambiguation: {e.options[:5]}"
+            return f"Disambiguation error: multiple results found: {', '.join(e.options[:5])}"
         except wikipedia.PageError:
-            return "No page found."
+            return "Wikipedia page not found."
         except Exception as e:
-            return f"Unexpected error: {str(e)}"
+            return f"Error while retrieving Wikipedia content: {str(e)}"
 
-    def is_historical_country(self, topic: str) -> str:
-        try:
-            summary = wikipedia.summary(topic, sentences=2).lower()
-            keywords = [
-                "former country", "no longer exists", "historical country",
-                "was a country", "defunct", "dissolved", "existed until",
-                "disestablished", "merged into"
-            ]
-            return "yes" if any(k in summary for k in keywords) else "no"
-        except:
-            return "no"
-
-wiki_tool = WikiTool()
-excel_tool = ExcelAnalysisTool()
+
+wiki_tool = WikipediaQAToolTool()
+#excel_tool = ExcelAnalysisTool()
 
 async def run_and_submit_all(profile: gr.OAuthProfile | None):
     log_output = ""
@@ -121,7 +82,7 @@ async def run_and_submit_all(profile: gr.OAuthProfile | None):
     try:
         
         agent = ToolCallingAgent(
-            tools=[search_tool, wiki_tool, excel_tool],
+            tools=[search_tool, wiki_tool],
                         model=OpenAIServerModel(
                     model_id="gpt-4o",            # ✅ valid OpenAI model name
                     temperature=0.0,