first agent

agent/graph.py

@@ -0,0 +1,35 @@
+from langgraph.graph import StateGraph, MessagesState, START, END
+from langchain_core.messages import HumanMessage, AIMessage
+from langgraph.prebuilt import ToolNode, tools_condition
+
+from tools import TOOLS
+
+class State(MessagesState):
+    file_name: str
+
+def retriever(state: State):
+    if state.file_name:
+        # Simulate file retrieval
+        return {"file_content": f"Retrieved content from {state.file_name}"}
+
+def call_model(state: State):
+    return {"messages": [AIMessage(content="Hello! How can I assist you today?")]}
+
+
+def build_agent():
+    graph_builder = StateGraph(MessagesState)
+
+    graph_builder.add_node("call_model", call_model)
+    graph_builder.add_node("tools", ToolNode(TOOLS))
+    graph_builder.add_edge(START, "call_model")
+    graph_builder.add_conditional_edges("call_model", tools_condition)
+    graph_builder.add_edge("tools", "call_model")
+
+    return graph_builder.compile()
+
+if __name__ == "__main__":
+    # Example usage
+    agent = build_agent()
+    output = agent.invoke({"messages": [HumanMessage(content="Hello, how are you?")]})
+    for msg in output["messages"]:
+        msg.pretty_print()

agent/tools.py

@@ -0,0 +1,80 @@
+from typing import List
+from langchain_core.tools import tool
+from langchain_community.document_loaders import WikipediaLoader, YoutubeLoader
+from langchain_community.tools import DuckDuckGoSearchResults
+from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
+@tool
+def wikipedia_search(query: str) -> str:
+    """
+    Search Wikipedia for a given query and return max 2 results.
+
+    Args:
+        query: The search query.
+    """
+    # Simulate a search operation
+    search_docs = WikipediaLoader(query=query, load_max_docs=2).load()
+
+    formatted_docs = "\n\n---\n\n".join(
+        [
+            f'<Document title="{doc.metadata["title"]}"/>\n{doc.page_content}\n</Document>'
+            for doc in search_docs
+        ])
+    return formatted_docs
+
+@tool
+def youtube_transcript(url: str) -> str:
+    """"Returns the transcript of a YouTube video given its URL.
+    Args:
+        url: The YouTube video URL.
+    """
+    try:
+        transcripts = YoutubeLoader.from_youtube_url(url, add_video_info=False).load()
+        return f"Video Transcript: {transcripts[0].page_content}"
+    except Exception as e:
+        return "No transcript available for this video. Error: {e}"
+    
+wrapper = DuckDuckGoSearchAPIWrapper(max_results=5)
+
+search = DuckDuckGoSearchResults(output_format="list", api_wrapper=wrapper)
+    
+@tool
+def web_search(query: str) -> str:
+    """
+    Perform a web search for the given query and return the results.
+    Use this when you need to find current or factual information.
+    Args:
+        query: The search query.
+    """
+    # Simulate a web search operation
+    query = "obama"
+    search_results = search.invoke(query)
+    formatted_result = "\n\n---\n\n".join([
+        f"- {result['title']}: {result['link']} \n {result['snippet']}"
+        for result in search_results
+    ])
+    return f"Web search results for '{query}'"
+
+@tool
+def add_numbers(numbers: List[float]) -> float:
+    """
+    Add a list of numbers together. E.g [1, 2, 3] -> 6
+    Args:
+        numbers: A list of numbers to add.
+    """
+    return sum(numbers)
+
+@tool
+def multiply_numbers(numbers: List[float]) -> float:
+    """
+    Multiply a list of numbers together. E.g [3, 2, 3] -> 18
+    Args:
+        numbers: A list of numbers to multiply.
+    """
+    result = 1
+    for number in numbers:
+        result *= number
+    return result
+
+
+
+TOOLS = [wikipedia_search, web_search, youtube_transcript, add_numbers, multiply_numbers]

app.py

@@ -1,9 +1,13 @@
+from io import BytesIO
 import os
 import gradio as gr
 import requests
 import inspect
 import pandas as pd
 
+from langchain_core.messages import HumanMessage
+from agent.graph import build_agent
+
 # (Keep Constants as is)
 # --- Constants ---
 DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
@@ -12,12 +16,21 @@ DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
 class BasicAgent:
     def __init__(self):
-        print("BasicAgent initialized.")
-    def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        fixed_answer = "This is a default answer."
-        print(f"Agent returning fixed answer: {fixed_answer}")
-        return fixed_answer
+        self.agent = build_agent()
+    def __call__(self, question: str, task_id: str, file_name="") -> str:
+        messages = [HumanMessage(content=question)]
+
+        if file_name:
+            task_id = "cca530fc-4052-43b2-b130-b30968d8aa44"
+            response = requests.get(f"{DEFAULT_API_URL}/files/{task_id}", timeout=15)
+            response.raise_for_status()
+            file_data = response.content
+            #file_data = BytesIO(file_data)
+
+
+        state = self.agent.invoke({"messages": messages, "file_name": file_name})
+        answer = state["messages"][-1].content
+        return answer
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
@@ -76,11 +89,12 @@ def run_and_submit_all( profile: gr.OAuthProfile | None):
     for item in questions_data:
         task_id = item.get("task_id")
         question_text = item.get("question")
+        file_name = item.get("file_name")
         if not task_id or question_text is None:
             print(f"Skipping item with missing task_id or question: {item}")
             continue
         try:
-            submitted_answer = agent(question_text)
+            submitted_answer = agent(question_text, file_name=file_name)
             answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
             results_log.append({"Task ID": task_id, "Question": question_text, "Submitted Answer": submitted_answer})
         except Exception as e:

requirements.txt

@@ -1,2 +1,9 @@
 gradio
-requests
+requests
+langgraph
+langchain
+langchain-community
+wikipedia
+youtube-transcript-api
+duckduckgo-search
+docling