removed app file

app.py

@@ -1,110 +0,0 @@
-import os
-# Import the load_dotenv function from the dotenv library
-from dotenv import load_dotenv
-from langchain_google_genai import ChatGoogleGenerativeAI
-
-from multimodal_tools import extract_text_tool, analyze_image_tool, analyze_audio_tool
-
-# Load environment variables from .env file
-load_dotenv()
-# Read your API key from the environment variable or set it manually
-api_key = os.getenv("GEMINI_API_KEY")
-langfuse_secret_key = os.getenv("LANGFUSE_SECRET_KEY")
-langfuse_public_key = os.getenv("LANGFUSE_PUBLIC_KEY")
-
-from typing import TypedDict, Annotated
-from langgraph.graph.message import add_messages
-from langchain_core.messages import AnyMessage, HumanMessage, AIMessage
-from langgraph.prebuilt import ToolNode
-from langgraph.graph import START, StateGraph
-from langgraph.prebuilt import tools_condition
-from langchain_community.tools.tavily_search import TavilySearchResults # Importa Tavily
-from langchain_community.tools import DuckDuckGoSearchRun
-# from langfuse import Langfuse # Langfuse is initialized by CallbackHandler directly
-from langfuse.callback import CallbackHandler
-from youtube_tools import youtube_transcript_tool
-from math_tools import add_tool, subtract_tool, multiply_tool, divide_tool
-from serpapi_tools import serpapi_search_tool
-from IPython.display import Image, display
-# Generate thfrom langchain_community.tools.tavily_search import TavilySearchResults
-from langchain_community.tools.tavily_search import TavilySearchResults
-
-
-# Initialize Langfuse CallbackHandler for LangGraph/Langchain (tracing)
-langfuse_handler = CallbackHandler(
-    public_key=langfuse_public_key,
-    secret_key=langfuse_secret_key,
-    host="http://localhost:3000"
-)
-
-# Create LLM class
-chat = ChatGoogleGenerativeAI(
-    model= "gemini-2.5-pro-preview-05-06",
-    temperature=0,
-    max_retries=2,
-    google_api_key=api_key,
-    thinking_budget= 0
-)
-
-search_tool = TavilySearchResults(
-    name="tavily_web_search", # Puoi personalizzare il nome se vuoi
-    description="Esegue una ricerca web avanzata utilizzando Tavily per informazioni aggiornate e complete. Utile per domande complesse o che richiedono dati recenti. Può essere utile fare più ricerche modificando la query per ottenere risultati migliori.", # Descrizione per l'LLM
-    max_results=5
-)
-
-tools = [
-    extract_text_tool,
-    analyze_image_tool,
-    analyze_audio_tool,
-    youtube_transcript_tool,
-    add_tool,
-    subtract_tool,
-    multiply_tool,
-    divide_tool,
-    search_tool
-]
-chat_with_tools = chat.bind_tools(tools)
-
-
-class AgentState(TypedDict):
-    messages: Annotated[list[AnyMessage], add_messages]
-
-def assistant(state: AgentState):
-    sys_msg = "You are a helpful assistant with access to tools. Understand user requests accurately. Use your tools when needed to answer effectively. Strictly follow all user instructions and constraints." \
-    "Pay attention: your output needs to contain only the final answer without any reasoning since it will be strictly evaluated against a dataset which contains only the specific response." \
-    "Your final output needs to be just the string or integer containing the answer, not an array or technical stuff."
-    return {
-        "messages": [chat_with_tools.invoke([sys_msg] + state["messages"])]
-    }
-
-    
-## The graph
-builder = StateGraph(AgentState)
-
-# Define nodes: these do the work
-builder.add_node("assistant", assistant)
-builder.add_node("tools", ToolNode(tools))
-
-# Define edges: these determine how the control flow moves
-builder.add_edge(START, "assistant")
-builder.add_conditional_edges(
-    "assistant",
-    # If the latest message requires a tool, route to tools
-    # Otherwise, provide a direct response
-    tools_condition,
-)
-builder.add_edge("tools", "assistant")
-alfred = builder.compile()
-
-""" # Salva l'immagine del grafo su un file
-graph_image_bytes = alfred.get_graph(xray=True).draw_mermaid_png()
-with open("alfred_graph.png", "wb") as f:
-    f.write(graph_image_bytes)
-print("L'immagine del grafo è stata salvata come alfred_graph.png")
-
-messages = [HumanMessage(content="Who did the actor who played Ray in the Polish-language version of Everybody Loves Raymond play in Magda M.? Give only the first name.")]
-response = alfred.invoke(input={"messages": messages}, config={"callbacks": [langfuse_handler]})
-
-print("🎩 Alfred's Response:")
-print(response['messages'][-1].content)
- """