Update app.py

app.py

@@ -11,20 +11,12 @@ from llama_index.core.agent import ReActAgent
 from llama_index.core.agent.workflow import AgentWorkflow
 from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
 from youtube_tool import youtube_transcript_tool, youtube_transcript_snippet_tool
-from multiple_tools import round_to_two_decimals_tool, text_inverter_tool, google_web_search_tool, wikipedia_search_tool
+#from multiple_tools import round_to_two_decimals_tool, text_inverter_tool, google_web_search_tool, wikipedia_search_tool
+from multiple_tools import round_to_two_decimals_tool, text_inverter_tool, google_web_search_tool, wikipedia_search_tool, transcribe_audio_tool, excel_food_sales_sum_tool, parse_file_and_summarize_tool, solve_chess_image_tool, vegetable_classifier_tool
 from agent import smart_agent
 from llama_index.llms.openai import OpenAI
 #-----------------------------------------------------------------
-from langgraph_impl.my_agent import create_langchain_agent
-#from langchain_community.llms import OpenAI
-from langchain.tools import Tool
-from langgraph_impl.my_agent import create_langchain_agent
 
-from langgraph_impl.wikipedia_tool import wiki_search
-from langgraph_impl.youtube_tool import get_youtube_transcript
-from langgraph_impl.audio_transcriber import transcribe_audio
-from langgraph_impl.image_chess_solver import solve_chess_image
-from langgraph_impl.file_parser import parse_file_and_summarize
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -33,49 +25,40 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 # --- Basic Agent Definition ---
 
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
-# class BasicAgent:
-#     def __init__(self):
-#         print("BasicAgent initialized. . . .")
-#         # self.agent = smart_agent()
-#         # self.api_key = os.getenv("HF_TOKEN")
-#         OpenAI_key = os.getenv("OPEN_AI_TOKEN")
-#         self.llm = OpenAI(model="gpt-4o-mini", temperature=0.2, api_key=OpenAI_key)
-#         self.agent = AgentWorkflow.from_tools_or_functions(
-#             [wikipedia_search_tool, youtube_transcript_tool, youtube_transcript_snippet_tool, round_to_two_decimals_tool, text_inverter_tool, google_web_search_tool],
-#             llm=self.llm,
-#         )
-
-#     async def run(self, question: str) -> str:
-#         print(f"Agent received question (first 50 chars): {question[:50]}...")
-#         # answer = await self.agent.run(question)
-#         answer = await self.agent.run(
-#             f"{question}\n\nIf you have enough information, respond with a concise final answer.",
-#             max_iterations=50
-#         )
-#         if hasattr(answer, "output"):
-#             print(f"Agent returning answer: {answer}")
-#             return str(answer.output)
-#         else:
-#             print(f"Agent returning answer: {answer}")
-#             return str(answer)
-        
-#     def __call__(self, question: str) -> str:
-#         return asyncio.run(self.run(question))
-
-
 class BasicAgent:
     def __init__(self):
-        print("Initializing LangChain Agent...")
-        self.agent = create_langchain_agent()
-
+        print("BasicAgent initialized. . . .")
+        # self.agent = smart_agent()
+        # self.api_key = os.getenv("HF_TOKEN")
+        OpenAI_key = os.getenv("OPEN_AI_TOKEN")
+        self.llm = OpenAI(model="gpt-4o-mini", temperature=0.2, api_key=OpenAI_key)
+        # self.agent = AgentWorkflow.from_tools_or_functions(
+        #     [wikipedia_search_tool, youtube_transcript_tool, youtube_transcript_snippet_tool, round_to_two_decimals_tool, text_inverter_tool, google_web_search_tool],
+        #     llm=self.llm,
+        # )
+        self.agent = AgentWorkflow.from_tools_or_functions(
+            [
+                wikipedia_search_tool, youtube_transcript_tool, youtube_transcript_snippet_tool, round_to_two_decimals_tool, text_inverter_tool, google_web_search_tool,transcribe_audio_tool, excel_food_sales_sum_tool, parse_file_and_summarize_tool, solve_chess_image_tool, vegetable_classifier_tool
+             ],
+            llm=self.llm
+        )
+    async def run(self, question: str) -> str:
+        print(f"Agent received question (first 50 chars): {question[:50]}...")
+        # answer = await self.agent.run(question)
+        answer = await self.agent.run(
+            f"{question}\n\nIf you have enough information, respond with a concise final answer.",
+            max_iterations=50
+        )
+        if hasattr(answer, "output"):
+            print(f"Agent returning answer: {answer}")
+            return str(answer.output)
+        else:
+            print(f"Agent returning answer: {answer}")
+            return str(answer)
+        
     def __call__(self, question: str) -> str:
-        try:
-            result = self.agent.invoke({"input": question})
-            if isinstance(result, dict) and "output" in result:
-                return result["output"]
-            return result
-        except Exception as e:
-            return f"Agent error: {e}"
+        return asyncio.run(self.run(question))
+