Update app.py

app.py

@@ -1,20 +1,12 @@
 import os
-import spaces
 import gradio as gr
 import requests
 import inspect
 import pandas as pd
 
-import base64
-from langchain_community.tools import DuckDuckGoSearchRun
-from typing import TypedDict, Annotated, Optional, List
-from langgraph.graph.message import add_messages
-from langgraph.graph import START, StateGraph
-from langgraph.prebuilt import ToolNode, tools_condition
-from langchain_core.messages import AnyMessage, SystemMessage, HumanMessage
-from langchain_core.tools import Tool
-from IPython.display import Image, display
-from langchain_google_genai import ChatGoogleGenerativeAI
+import spaces
+from agent import graph
+from langchain_core.messages import HumanMessage
 
 # (Keep Constants as is)
 # --- Constants ---
@@ -28,117 +20,19 @@ def gpu_check():
 
 # --- Basic Agent Definition ---
 # ----- THIS IS WERE YOU CAN BUILD WHAT YOU WANT ------
+
+
 class BasicAgent:
     def __init__(self):
         print("BasicAgent initialized.")
-
-        # --- Tools ---
-        def _search_web(query:str) -> str:
-            """Searches the web for the answer to a given question or topic."""
-            return DuckDuckGoSearchRun().run(query)
-
-        def _extract_text(img_path: str) -> str:
-            """
-            Extract text from an image file using a multimodal model.
-            Get information from the image including objects, people and/or weather
-            """
-            all_text = ""
-            try:
-                # Read image and encode as base64
-                with open(img_path, "rb") as image_file:
-                    image_bytes = image_file.read()
-
-                image_base64 = base64.b64encode(image_bytes).decode("utf-8")
-
-                # Prepare the prompt including the base64 image data
-                message = [
-                    HumanMessage(
-                        content=[
-                            {
-                                "type": "text",
-                                "text": (
-                                    "Extract all the text from this image. Also extract any objects, people and/or weather"
-                                    "Return only the extracted text, no explanations."
-                                ),
-                            },
-                            {
-                                "type": "image_url",
-                                "image_url": {
-                                    "url": f"data:image/png;base64,{image_base64}"
-                                },
-                            },
-                        ]
-                    )
-                ]
-
-                # Call the vision-capable model
-                vision_llm = ChatGoogleGenerativeAI(model="gemini-3.7-flash")
-                response = vision_llm.invoke(message)
-
-                # Append extracted text
-                all_text += response.content + "\n\n"
-
-                return all_text.strip()
-            except Exception as e:
-                # A butler should handle errors gracefully
-                error_msg = f"Error extracting text: {str(e)}"
-                print(error_msg)
-                return ""
-
-
-        # --- Agent ---
-        class AgentState(TypedDict):
-            messages: Annotated[list[AnyMessage], add_messages]
-
-        chat = ChatGoogleGenerativeAI(
-            model="gemini-3.7-flash"
-        )
-        # Initialize the tools
-        describe_image = Tool(
-            name = "describe_image",
-            func = _extract_text,
-            description = "Extracts text from an image file."
-        )
-        search_web = Tool(
-            name = "search_web",
-            func = _search_web,
-            description = "Searches the web for information."
-        )
-        tools = [search_web, describe_image]
-        chat_with_tools = chat.bind_tools(tools)
-
-
-        def assistant(state: AgentState):
-            return {
-                "messages": [chat_with_tools.invoke(state["messages"])],
-            }
-
-        ## The graph
-        workflow = StateGraph(AgentState)
-
-        # Define nodes: these do the work
-        workflow.add_node("assistant", assistant)
-        workflow.add_node("tools", ToolNode(tools))
-
-        # Define edges: these determine how the control flow moves
-        workflow.add_edge(START, "assistant")
-        workflow.add_conditional_edges(
-            "assistant",
-            # If the latest message requires a tool, route to tools
-            # Otherwise, provide a direct response
-            tools_condition,
-        )
-        workflow.add_edge("tools", "assistant")
-        self.alfred = workflow.compile()
-
-
-
     def __call__(self, question: str) -> str:
-        print(f"Agent received question (first 50 chars): {question[:50]}...")
-        response = self.alfred.invoke({"messages": question})
-        answer = response['messages'][-1].text
-        print(f"Agent returning answer: {answer}")
-        return answer
+        answer = graph.invoke({
+            "messages": [HumanMessage(content=question)],
+            "file_path": None,
+            "url": None,
+            "task_id": None
+        })
+        return answer["messages"][-1].content
 
 def run_and_submit_all( profile: gr.OAuthProfile | None):
     """
@@ -267,9 +161,11 @@ with gr.Blocks() as demo:
     gr.Markdown(
         """
         **Instructions:**
+
         1.  Please clone this space, then modify the code to define your agent's logic, the tools, the necessary packages, etc ...
         2.  Log in to your Hugging Face account using the button below. This uses your HF username for submission.
         3.  Click 'Run Evaluation & Submit All Answers' to fetch questions, run your agent, submit answers, and see the score.
+
         ---
         **Disclaimers:**
         Once clicking on the "submit button, it can take quite some time ( this is the time for the agent to go through all the questions).