Update app.py

app.py

@@ -7,8 +7,7 @@ from typing import List, Dict, Union
 import requests
 import wikipediaapi
 import pandas as pd
-from langchain_core.messages import HumanMessage
-from agent import build_graph
+from duckduckgo_search import DDGS
 
 load_dotenv()
 
@@ -17,24 +16,84 @@ load_dotenv()
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 
 
-# --- Basic Agent Definition ---
+# Custom search tool class
+class CustomDuckDuckGoSearchTool:
+    def __call__(self, query: str, max_results: int = 5):
+        try:
+            with DDGS() as ddgs:
+                results = []
+                for r in ddgs.text(query):
+                    results.append(r)
+                    if len(results) >= max_results:
+                        break
+            return results
+        except Exception as e:
+            return f"Search error: {str(e)}"
+
+# Dummy placeholder for `visit_webpage` tool
+class VisitWebpageTool:
+    def __call__(self, url: str):
+        return f"Pretending to visit: {url}"
+
+# Final answer tool to format and return the final response
+class FinalAnswerTool:
+    def __call__(self, results):
+        formatted_answer = "Final Answer:\n"
+        for result in results:
+            formatted_answer += f"- {str(result)}\n"
+        return formatted_answer
+
+# Dummy model
+class DummyModel:
+    def call(self, input_text):
+        return f"Model processing: {input_text}"
+
+# Modified ToolCallingAgent to use FinalAnswerTool
+class ToolCallingAgent:
+    def __init__(self, tools, model, final_answer_tool, max_steps=10):
+        self.tools = tools
+        self.model = model
+        self.final_answer_tool = final_answer_tool
+        self.max_steps = max_steps
 
+    def run(self, query):
+        print(f"Running agent with query: {query}")
+        tool_outputs = []
+        for tool in self.tools:
+            output = tool(query)
+            print("Tool output:", output)
+            tool_outputs.append(output)
+        # Use the final answer tool to format the collected outputs
+        final_result = self.final_answer_tool(tool_outputs)
+        print(final_result)
+        return final_result
 
+# Initialize tools and model
+model = DummyModel()
+search_tool = CustomDuckDuckGoSearchTool()
+visit_webpage = VisitWebpageTool()
+final_answer = FinalAnswerTool()
+
+# Initialize the agent
+web_agent = ToolCallingAgent(
+    tools=[search_tool, visit_webpage],
+    model=model,
+    final_answer_tool=final_answer,
+    max_steps=10
+)
+
+# Example usage
+#web_agent.run("Latest AI tools")
+
+# --- Basic Agent Definition ---
 class BasicAgent:
-    """A langgraph agent."""
     def __init__(self):
         print("BasicAgent initialized.")
-        self.graph = build_graph()
-
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        # Wrap the question in a HumanMessage from langchain_core
-        messages = [HumanMessage(content=question)]
-        messages = self.graph.invoke({"messages": messages})
-        answer = messages['messages'][-1].content
-        return answer[14:]
-
-
+        fixed_answer = web_agent.run({question})
+        print(f"Agent returning fixed answer: {fixed_answer}")
+        return fixed_answer
 
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):