fix: try again

app.py

@@ -2,12 +2,20 @@ import os
 import gradio as gr
 import requests
 import inspect
+from llama_index.llms.ollama import Ollama
 from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
 from llama_index.readers.youtube_transcript import YoutubeTranscriptReader
 from llama_index.core.tools import FunctionTool
 from llama_index.tools.duckduckgo import DuckDuckGoSearchToolSpec
 from llama_index.core.agent.workflow import AgentWorkflow
 from llama_index.llms.gemini import Gemini
+from llama_index.retrievers.bm25 import BM25Retriever
+from llama_index.core.query_engine import RetrieverQueryEngine
+from llama_index.core.tools import QueryEngineTool
+from llama_index.core.node_parser import SentenceSplitter
+from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI
+from llama_index.core.schema import Document
+from llama_index.core import get_response_synthesizer
 import pandas as pd
 import asyncio
 
@@ -19,15 +27,15 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
     def __init__(self):
-        self.llm = Gemini(model_name="models/gemini-2.0-flash-thinking-exp-01-21")
+        # self.llm = Ollama(model="qwen2.5:7b", request_timeout=500)
+        self.llm = Gemini(model_name="models/gemini-2.0-flash-lite")
         print("BasicAgent initialized.")
     def __call__(self, question: str) -> str:
         print(f"Agent received question (first 50 chars): {question[:50]}...")
-        def get_answers_from_youtube_transcript(youtube_link: str, query: str) -> str:
+        def get_youtube_transcript(youtube_link: str) -> str:
             """Fetches transcript of the given youtube_link and returns matching answers based on query.
                 Args:
                 youtube_link (str): youtube video link for which we need to answer questions on.
-                query (str): question to answer from video transcript.
             """
             loader = YoutubeTranscriptReader()
             documents = loader.load_data(
@@ -39,21 +47,46 @@ class BasicAgent:
             return "Here's the transcript from the video, examine and formulate answer based on what is said in the transcript: \n" + text
 
         youtube_transcript_answer_tool = FunctionTool.from_defaults(
-            get_answers_from_youtube_transcript,
-            name="get_answers_from_youtube_transcript",
-            description="Fetches transcript of the given youtube_link and returns matching answers based on query.",
+            get_youtube_transcript,
+            name="get_youtube_transcript",
+            description="Fetches transcript of the given video using youtube_link.",
         )
 
-        duckduckgoSearchTools = DuckDuckGoSearchToolSpec().to_tool_list()
+        def duck_duck_go_search_tool(query: str) -> str:
+            try:
+                raw_results = DuckDuckGoSearchToolSpec().duckduckgo_full_search(query)
+                texts = [res['body'] for res in raw_results]
+                documents = [Document(text=body) for body in texts]
+
+                splitter = SentenceSplitter(chunk_size=256)
+                nodes = splitter.get_nodes_from_documents(documents)
+
+                retriever = BM25Retriever(nodes=nodes, similarity_top_k=10)
+                synthesizer = get_response_synthesizer(response_mode="refine", llm=self.llm)
+                query_engine = RetrieverQueryEngine(retriever=retriever, response_synthesizer=synthesizer)
+
+                response = query_engine.query(query)
+                return response.response
+
+            except Exception as e:
+                return f"An error occurred: {e}"
+
+
+        duckduckgo_search_tool = FunctionTool.from_defaults(
+                duck_duck_go_search_tool,
+                name="duck_duck_go_search_tool",
+                description="Searches the web and refines the result into a high-quality answer."
+            )
 
-        agent = AgentWorkflow.from_tools_or_functions([*duckduckgoSearchTools, youtube_transcript_answer_tool], llm=self.llm)
+        agent = AgentWorkflow.from_tools_or_functions([duckduckgo_search_tool, youtube_transcript_answer_tool], llm=self.llm)
 
         async def run_agent():
             return await agent.run(question)
         
-        fixed_answer = asyncio.run(run_agent())
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+        response = asyncio.run(run_agent())
+        final_answer = response.response.blocks[0].text
+        print(f"Agent returning fixed answer: {final_answer}")
+        return final_answer
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """