agent.py

"""
agent.py

This file defines the core logic for a sophisticated AI agent using LangGraph.
## MODIFICATION: This version introduces a 'multimodal_router' node.
This node intelligently inspects user input to identify, classify (using HEAD requests),
and pre-process URLs for images, audio, and video before the main LLM reasoning step.
"""

# ----------------------------------------------------------
# Section 0: Imports and Configuration
# ----------------------------------------------------------
import json
import os
import pickle
import re
import subprocess
import textwrap
import base64
import functools
from io import BytesIO
from pathlib import Path
import tempfile
import yt_dlp
from pydub import AudioSegment
import speech_recognition as sr

import requests
from cachetools import TTLCache
from PIL import Image

from langchain.schema import Document
from langchain.tools.retriever import create_retriever_tool
from langchain_community.vectorstores import FAISS
from langchain_community.tools.tavily_search import TavilySearchResults
from langchain_community.document_loaders import WikipediaLoader, ArxivLoader
from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
from langchain_core.tools import Tool, tool
from langchain_google_genai import ChatGoogleGenerativeAI
from langchain_groq import ChatGroq
from langchain_huggingface import HuggingFaceEmbeddings
from langgraph.graph import START, StateGraph, MessagesState
from langgraph.prebuilt import ToolNode, tools_condition

from dotenv import load_dotenv
load_dotenv()

# --- Configuration and Caching (remains the same) ---
JSONL_PATH, FAISS_CACHE, EMBED_MODEL = Path("metadata.jsonl"), Path("faiss_index.pkl"), "sentence-transformers/all-mpnet-base-v2"
RETRIEVER_K, CACHE_TTL = 5, 600
API_CACHE = TTLCache(maxsize=256, ttl=CACHE_TTL)
def cached_get(key: str, fetch_fn):
    if key in API_CACHE: return API_CACHE[key]
    val = fetch_fn()
    API_CACHE[key] = val
    return val

# ----------------------------------------------------------
# Section 2: Standalone Tool Functions (remains the same)
# ----------------------------------------------------------
@tool
def python_repl(code: str) -> str:
    """Executes a string of Python code and returns the stdout/stderr."""
    # ... (implementation unchanged)
    code = textwrap.dedent(code).strip()
    try:
        result = subprocess.run(["python", "-c", code], capture_output=True, text=True, timeout=10, check=False)
        if result.returncode == 0: return f"Execution successful.\nSTDOUT:\n```\n{result.stdout}\n```"
        else: return f"Execution failed.\nSTDOUT:\n```\n{result.stdout}\n```\nSTDERR:\n```\n{result.stderr}\n```"
    except subprocess.TimeoutExpired: return "Execution timed out (>10s)."


@tool
def process_youtube_video(url: str) -> str:
    """Downloads and processes a YouTube video, extracting audio and converting to text."""
    # ... (implementation unchanged)
    try:
        print(f"Processing YouTube video: {url}")
        with tempfile.TemporaryDirectory() as temp_dir:
            ydl_opts = {
                'format': 'bestaudio/best', 'outtmpl': f'{temp_dir}/%(title)s.%(ext)s',
                'postprocessors': [{'key': 'FFmpegExtractAudio', 'preferredcodec': 'wav'}],
            }
            with yt_dlp.YoutubeDL(ydl_opts) as ydl:
                info = ydl.extract_info(url, download=True)
                title = info.get('title', 'Unknown')
            audio_files = list(Path(temp_dir).glob("*.wav"))
            if not audio_files: return "Error: Could not download audio from YouTube video"
            r, transcript_parts = sr.Recognizer(), []
            audio = AudioSegment.from_wav(str(audio_files[0])).set_channels(1).set_frame_rate(16000)
            chunks = [audio[i:i + 30000] for i in range(0, len(audio), 30000)]
            for i, chunk in enumerate(chunks[:10]):
                chunk_file = Path(temp_dir) / f"chunk_{i}.wav"
                chunk.export(chunk_file, format="wav")
                try:
                    with sr.AudioFile(str(chunk_file)) as source:
                        text = r.recognize_google(r.record(source))
                        transcript_parts.append(text)
                except (sr.UnknownValueError, sr.RequestError) as e:
                    transcript_parts.append(f"[Speech recognition error or unintelligible audio: {e}]")
            return f"YouTube Video: {title}\n\nTranscript (first 5 minutes):\n{' '.join(transcript_parts)}"
    except Exception as e:
        print(f"Error processing YouTube video: {e}")
        return f"Error processing YouTube video: {e}"

@tool
def process_audio_file(file_url: str) -> str:
    """Downloads and processes an audio file (MP3, WAV, etc.) and converts to text."""
    # ... (implementation unchanged)
    try:
        print(f"Processing audio file: {file_url}")
        with tempfile.TemporaryDirectory() as temp_dir:
            response = requests.get(file_url, timeout=30)
            response.raise_for_status()
            ext = os.path.splitext(file_url)[1][1:] or 'mp3'
            audio_file = Path(temp_dir) / f"audio.{ext}"
            with open(audio_file, 'wb') as f: f.write(response.content)
            wav_file = Path(temp_dir) / "audio.wav"
            AudioSegment.from_file(str(audio_file)).export(wav_file, format="wav")
            r, transcript_parts = sr.Recognizer(), []
            audio = AudioSegment.from_wav(str(wav_file)).set_channels(1).set_frame_rate(16000)
            chunks = [audio[i:i + 30000] for i in range(0, len(audio), 30000)]
            for i, chunk in enumerate(chunks[:20]):
                chunk_file = Path(temp_dir) / f"chunk_{i}.wav"
                chunk.export(chunk_file, format="wav")
                try:
                    with sr.AudioFile(str(chunk_file)) as source:
                        text = r.recognize_google(r.record(source))
                        transcript_parts.append(text)
                except (sr.UnknownValueError, sr.RequestError) as e:
                    transcript_parts.append(f"[Speech recognition error or unintelligible audio: {e}]")
            return f"Audio file transcript:\n{' '.join(transcript_parts)}"
    except Exception as e:
        print(f"Error processing audio file: {e}")
        return f"Error processing audio file: {e}"


def web_search_func(query: str, cache_func) -> str:
    """Performs a web search using Tavily and returns a compilation of results."""
    # ... (implementation unchanged)
    key = f"web:{query}"
    results = cache_func(key, lambda: TavilySearchResults(max_results=5).invoke(query))
    return "\n\n---\n\n".join([f"Source: {res['url']}\nContent: {res['content']}" for res in results])

def wiki_search_func(query: str, cache_func) -> str:
    """Searches Wikipedia and returns the top 2 results."""
    # ... (implementation unchanged)
    key = f"wiki:{query}"
    docs = cache_func(key, lambda: WikipediaLoader(query=query, load_max_docs=2, doc_content_chars_max=2000).load())
    return "\n\n---\n\n".join([f"Source: {d.metadata['source']}\n\n{d.page_content}" for d in docs])

def arxiv_search_func(query: str, cache_func) -> str:
    """Searches Arxiv for scientific papers and returns the top 2 results."""
    # ... (implementation unchanged)
    key = f"arxiv:{query}"
    docs = cache_func(key, lambda: ArxivLoader(query=query, load_max_docs=2).load())
    return "\n\n---\n\n".join([f"Source: {d.metadata['source']}\nPublished: {d.metadata['Published']}\nTitle: {d.metadata['Title']}\n\nSummary:\n{d.page_content}" for d in docs])

# ----------------------------------------------------------
# Section 3: DYNAMIC SYSTEM PROMPT (remains the same)
# ----------------------------------------------------------
SYSTEM_PROMPT_TEMPLATE = (
    """You are an expert-level multimodal research assistant...""" # Unchanged
)

# ----------------------------------------------------------
# Section 4: Factory Function for Agent Executor
# ----------------------------------------------------------
def create_agent_executor(provider: str = "groq"):
    """
    Factory function to create and compile the LangGraph agent executor.
    """
    print(f"Initializing agent with provider: {provider}")

    # Step 1: Build LLM (remains the same)
    if provider == "groq": 
        llm = ChatGroq(model_name="llama-3.1-70b-vision-preview", temperature=0)
    else: 
        raise ValueError(f"Provider '{provider}' not currently configured for this router.")
        
    # Step 2: Build Retriever (remains the same, but will be called inside the router)
    embeddings = HuggingFaceEmbeddings(model_name=EMBED_MODEL)
    if FAISS_CACHE.exists():
        with open(FAISS_CACHE, "rb") as f: vector_store = pickle.load(f)
    else:
        # ... logic to build vector_store from JSONL or create empty ...
        docs = []
        if JSONL_PATH.exists():
            docs = [Document(page_content=f"Question: {rec['Question']}\n\nFinal answer: {rec['Final answer']}", metadata={"source": rec["task_id"]}) for rec in (json.loads(line) for line in open(JSONL_PATH, "rt", encoding="utf-8"))]
        if not docs:
            docs = [Document(page_content="Sample document", metadata={"source": "sample"})]
        vector_store = FAISS.from_documents(docs, embeddings)
        with open(FAISS_CACHE, "wb") as f: pickle.dump(vector_store, f)
    retriever = vector_store.as_retriever(search_kwargs={"k": RETRIEVER_K})
    
    # Step 3: Create the final list of tools (remains the same)
    tools_list = [
        python_repl, process_youtube_video, process_audio_file,
        Tool(name="web_search", func=functools.partial(web_search_func, cache_func=cached_get), description="Performs a web search using Tavily."),
        Tool(name="wiki_search", func=functools.partial(wiki_search_func, cache_func=cached_get), description="Searches Wikipedia."),
        Tool(name="arxiv_search", func=functools.partial(arxiv_search_func, cache_func=cached_get), description="Searches Arxiv for scientific papers."),
        create_retriever_tool(retriever=retriever, name="retrieve_examples", description="Retrieve solved questions similar to the user's query."),
    ]
    
    # Step 4: Format prompt and bind tools (remains the same)
    tool_definitions = "\n".join([f"- `{tool.name}`: {tool.description}" for tool in tools_list])
    final_system_prompt = SYSTEM_PROMPT_TEMPLATE.format(tools=tool_definitions)
    llm_with_tools = llm.bind_tools(tools_list)

    # Step 5: Define Graph Nodes
    
    ## MODIFICATION: A new, powerful router node that replaces the previous pre-processing.
    def multimodal_router(state: MessagesState):
        """
        Inspects the user's message, classifies URLs, and prepares the state for the LLM.
        This node acts as a central dispatcher.
        """
        print("--- Entering Multimodal Router ---")
        messages = state["messages"]
        last_message = messages[-1]
        
        # 1. Perform knowledge base retrieval first
        # We consolidate this logic here from the old retriever_node
        user_query_text = ""
        if isinstance(last_message.content, str):
            user_query_text = last_message.content
        elif isinstance(last_message.content, list): # For multimodal messages
            user_query_text = " ".join(item['text'] for item in last_message.content if item['type'] == 'text')

        docs = retriever.invoke(user_query_text)
        system_messages = [SystemMessage(content=final_system_prompt)]
        if docs:
            example_text = "\n\n---\n\n".join(d.page_content for d in docs)
            system_messages.append(AIMessage(content=f"I have found {len(docs)} similar solved examples:\n\n{example_text}", name="ExampleRetriever"))
        
        # 2. Extract and classify URLs
        urls = re.findall(r'(https?://[^\s]+)', user_query_text)
        image_processed = False

        for url in urls:
            try:
                print(f"Routing URL: {url}")
                # Simple classification first
                if "youtube.com" in url or "youtu.be" in url:
                    system_messages.append(SystemMessage(content=f"[System Note: A YouTube URL has been detected. Use the 'process_youtube_video' tool if the user asks about it.]"))
                    continue
                
                # Use a HEAD request for robust classification
                headers = requests.head(url, timeout=5, allow_redirects=True).headers
                content_type = headers.get('Content-Type', '')
                
                if 'image/' in content_type and not image_processed:
                    print(f"  -> Classified as Image. Processing for vision model.")
                    response = requests.get(url, timeout=10)
                    response.raise_for_status()
                    img = Image.open(BytesIO(response.content))
                    buffered = BytesIO()
                    img.convert("RGB").save(buffered, format="JPEG")
                    b64_string = base64.b64encode(buffered.getvalue()).decode()
                    
                    # Embed the image into the last message
                    new_content = [
                        {"type": "text", "text": user_query_text},
                        {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{b64_string}"}}
                    ]
                    messages[-1] = HumanMessage(content=new_content)
                    image_processed = True # Process only the first image for now
                
                elif 'audio/' in content_type:
                    print(f"  -> Classified as Audio.")
                    system_messages.append(SystemMessage(content=f"[System Note: An audio URL has been detected. Use the 'process_audio_file' tool if the user asks about it.]"))
                
                else:
                    print(f"  -> Classified as Web Page/Other.")

            except Exception as e:
                print(f"  -> Could not process URL {url}: {e}")

        # Rebuild the final state
        final_messages = system_messages + messages
        return {"messages": final_messages}

    def assistant_node(state: MessagesState):
        result = llm_with_tools.invoke(state["messages"])
        return {"messages": [result]}

    # Step 6: Build Graph
    ## MODIFICATION: The graph is now simpler and more robust.
    builder = StateGraph(MessagesState)
    builder.add_node("multimodal_router", multimodal_router) # The new, powerful starting node
    builder.add_node("assistant", assistant_node)
    builder.add_node("tools", ToolNode(tools_list))
    
    builder.add_edge(START, "multimodal_router")
    builder.add_edge("multimodal_router", "assistant")
    builder.add_conditional_edges("assistant", tools_condition, {"tools": "tools", "__end__": "__end__"})
    builder.add_edge("tools", "assistant")

    agent_executor = builder.compile()
    print("Agent Executor with Multimodal Router created successfully.")
    return agent_executor