agent.py

"""
AI AGENT WITH LANGGRAPH + HUGGINGFACE INTEGRATION
Clean architecture with LangChain HuggingFace Pipeline
"""

import os
import json
import time
from typing import Dict, Any, List, Optional, Annotated
from dotenv import load_dotenv

from langchain_core.messages import HumanMessage, AIMessage, SystemMessage
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import JsonOutputParser, StrOutputParser

from langgraph.graph import StateGraph, END
from langgraph.graph.message import add_messages
from typing_extensions import TypedDict

from pydantic import BaseModel, Field

# LangChain HuggingFace Integration
from langchain_huggingface import HuggingFacePipeline, ChatHuggingFace, HuggingFaceEndpoint
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline

from utils import (
    process_question_with_tools,
    get_agent_state,
    reset_agent_state,
    ToolOrchestrator,
    get_system_prompt,
    get_response_prompt,
    build_context_summary,
    analyze_question_type
)

load_dotenv()

class AgentState(TypedDict):
    messages: Annotated[List, add_messages]
    question: str
    task_id: str
    ai_analysis: Dict[str, Any]
    should_use_tools: bool
    tool_processing_result: Dict[str, Any]
    final_answer: str
    processing_complete: bool

class QuestionAnalysis(BaseModel):
    question_type: str = Field(description="Type: youtube|image|audio|wiki|file|text|math")
    needs_tools: bool = Field(description="Whether tools are needed")
    reasoning: str = Field(description="AI reasoning for the decision")
    confidence: str = Field(description="Confidence level: high|medium|low")

class AIBrain:
    def __init__(self):
        self.model_name = "Qwen/Qwen3-8B"
        
        print("🧠 Initializing Qwen3-8B with LangChain HuggingFace...")
        
        # Load tokenizer with thinking disabled
        self.tokenizer = AutoTokenizer.from_pretrained(self.model_name)
        
        # Create text generation pipeline with Qwen3
        self.hf_pipeline = pipeline(
            "text-generation",
            model=self.model_name,
            tokenizer=self.tokenizer,
            torch_dtype="auto",
            device_map="auto",
            max_new_tokens=2048,
            temperature=0.7,
            top_p=0.9,
            do_sample=True,
            pad_token_id=self.tokenizer.eos_token_id if self.tokenizer.eos_token_id else self.tokenizer.pad_token_id
        )
        
        # Wrap with LangChain HuggingFacePipeline
        self.llm = HuggingFacePipeline(pipeline=self.hf_pipeline)
        
        # Create ChatHuggingFace for chat interface
        self.chat_model = ChatHuggingFace(llm=self.llm)
        
        print("✅ Qwen3 AI Brain with LangChain HuggingFace initialized")
    
    def _generate_with_qwen3(self, prompt: str, max_tokens: int = 2048) -> str:
        """Generate text with Qwen3 via LangChain - thinking disabled"""
        try:
            # Prepare messages for chat template with thinking DISABLED
            messages = [{"role": "user", "content": prompt}]
            
            # Apply chat template with enable_thinking=False
            text = self.tokenizer.apply_chat_template(
                messages,
                tokenize=False,
                add_generation_prompt=True,
                enable_thinking=False  # CRITICAL: Disable thinking mode
            )
            
            # Use LangChain HuggingFace pipeline for generation
            response = self.llm.invoke(text)
            
            # Clean up response - remove input prompt
            if text in response:
                response = response.replace(text, "").strip()
            
            return response
            
        except Exception as e:
            print(f"⚠️ Qwen3 generation error: {str(e)}")
            # Fallback to direct pipeline call
            try:
                result = self.hf_pipeline(prompt, max_new_tokens=max_tokens)
                return result[0]['generated_text'].replace(prompt, "").strip()
            except Exception as e2:
                return f"AI generation failed: {str(e2)}"
    
    def analyze_question(self, question: str, task_id: str = "") -> Dict[str, Any]:
        """Analyze question type using Qwen3 with strict JSON output"""
        
        prompt = f"""<instruction>
Analyze this question and determine the correct tool approach. Return ONLY valid JSON.
</instruction>

<question>{question}</question>
<task_id>{task_id}</task_id>

<classification_rules>
- YouTube URLs (youtube.com, youtu.be): "youtube"
- Images, photos, chess positions, visual content: "image" 
- Audio files, voice, sound, mp3: "audio"
- Excel, CSV, documents, file uploads: "file"
- Wikipedia searches, historical facts, people info: "wiki"
- Math calculations, logic, text analysis: "text"
</classification_rules>

Return this exact JSON format:
{{
    "question_type": "youtube|image|audio|wiki|file|text",
    "needs_tools": true,
    "reasoning": "Brief explanation of classification",
    "confidence": "high"
}}"""
        
        try:
            response = self._generate_with_qwen3(prompt, 512)
            
            # Extract JSON from response
            import re
            json_pattern = r'\{[^{}]*\}'
            json_match = re.search(json_pattern, response)
            
            if json_match:
                result = json.loads(json_match.group())
                
                # Validate required fields
                required_fields = ["question_type", "needs_tools", "reasoning", "confidence"]
                if all(field in result for field in required_fields):
                    return result
                    
            raise ValueError("Invalid JSON structure in response")
            
        except Exception as e:
            print(f"⚠️ Qwen3 analysis failed: {str(e)[:100]}...")
            
            # Fallback analysis
            question_type = analyze_question_type(question)
            return {
                "question_type": question_type,
                "needs_tools": question_type in ["wiki", "youtube", "image", "audio", "file"],
                "reasoning": f"Fallback classification: detected {question_type}",
                "confidence": "medium"
            }
    
    def generate_answer(self, question: str, tool_results: Dict[str, Any]) -> str:
        """Generate final answer using Qwen3 with context"""
        
        if tool_results and tool_results.get("tool_results"):
            context = build_context_summary(
                tool_results.get("tool_results", []),
                tool_results.get("cached_data", {})
            )
        else:
            context = "No additional context available"
        
        prompt = f"""<instruction>
Generate a comprehensive answer to the user's question using the provided context.
</instruction>

<question>{question}</question>

<context>
{context}
</context>

<output_rules>
- Provide direct, accurate answers
- Use context information when relevant
- Be concise but complete
- No thinking process in output
- Professional tone
</output_rules>

Answer:"""
        
        response = self._generate_with_qwen3(prompt, 2048)
        
        # Clean up response
        if "Answer:" in response:
            response = response.split("Answer:")[-1].strip()
        
        return response

# Initialize AI Brain globally
ai_brain = AIBrain()

def analyze_question_node(state: AgentState) -> AgentState:
    """Analyze question using Qwen3 AI Brain"""
    question = state["question"]
    task_id = state.get("task_id", "")
    
    print("🔍 Analyzing question with Qwen3...")
    analysis = ai_brain.analyze_question(question, task_id)
    
    state["ai_analysis"] = analysis
    state["should_use_tools"] = analysis.get("needs_tools", True)
    
    print(f"📊 Type: {analysis.get('question_type')} | Tools: {analysis.get('needs_tools')} | Confidence: {analysis.get('confidence')}")
    return state

def process_with_tools_node(state: AgentState) -> AgentState:
    """Process question with appropriate tools"""
    question = state["question"]
    task_id = state.get("task_id", "")
    
    print("🔧 Processing with specialized tools...")
    tool_results = process_question_with_tools(question, task_id)
    state["tool_processing_result"] = tool_results
    
    successful_tools = [result.tool_name for result in tool_results.get("tool_results", []) if result.success]
    if successful_tools:
        print(f"✅ Successful tools: {successful_tools}")
    else:
        print("⚠️ No tools succeeded")
    
    return state

def answer_directly_node(state: AgentState) -> AgentState:
    """Answer directly without tools using Qwen3"""
    question = state["question"]
    
    print("💭 Generating direct answer with Qwen3...")
    answer = ai_brain.generate_answer(question, {})
    state["final_answer"] = answer
    state["processing_complete"] = True
    
    return state

def generate_final_answer_node(state: AgentState) -> AgentState:
    """Generate final answer combining tool results and AI analysis"""
    question = state["question"]
    tool_results = state.get("tool_processing_result", {})
    
    print("🎯 Generating final answer with context...")
    answer = ai_brain.generate_answer(question, tool_results)
    state["final_answer"] = answer
    state["processing_complete"] = True
    
    return state

def create_agent_workflow():
    """Create LangGraph workflow for question processing"""
    workflow = StateGraph(AgentState)
    
    # Add nodes
    workflow.add_node("analyze_question", analyze_question_node)
    workflow.add_node("process_with_tools", process_with_tools_node) 
    workflow.add_node("answer_directly", answer_directly_node)
    workflow.add_node("generate_final_answer", generate_final_answer_node)
    
    # Define routing logic
    def should_use_tools(state: AgentState) -> str:
        return "process_with_tools" if state.get("should_use_tools", True) else "answer_directly"
    
    # Set up the flow
    workflow.set_entry_point("analyze_question")
    workflow.add_conditional_edges("analyze_question", should_use_tools)
    workflow.add_edge("process_with_tools", "generate_final_answer")
    workflow.add_edge("answer_directly", END)
    workflow.add_edge("generate_final_answer", END)
    
    return workflow.compile()

class LangGraphUtilsAgent:
    def __init__(self):
        self.app = create_agent_workflow()
        print("🚀 LangGraph Agent with Qwen3 + Utils System ready")
    
    def process_question(self, question: str, task_id: str = "") -> str:
        """Process question through the workflow"""
        try:
            print(f"\n🎯 Processing: {question[:100]}...")
            
            # Initialize state
            initial_state = {
                "messages": [HumanMessage(content=question)],
                "question": question,
                "task_id": task_id,
                "ai_analysis": {},
                "should_use_tools": True,
                "tool_processing_result": {},
                "final_answer": "",
                "processing_complete": False
            }
            
            # Run workflow
            start_time = time.time()
            result = self.app.invoke(initial_state)
            elapsed_time = time.time() - start_time
            
            final_answer = result.get("final_answer", "No answer generated")
            print(f"✅ Completed in {elapsed_time:.2f}s")
            
            return final_answer
            
        except Exception as e:
            print(f"❌ Agent error: {str(e)}")
            return f"I apologize, but I encountered an error processing your question: {str(e)}"

# Global agent instance
agent = LangGraphUtilsAgent()

def process_question(question: str, task_id: str = "") -> str:
    """Main entry point for question processing"""
    if not question or not question.strip():
        return "Please provide a valid question."
    
    return agent.process_question(question.strip(), task_id)

# =============================================================================
# TESTING
# =============================================================================

if __name__ == "__main__":
    print("🧪 Testing LangGraph Utils Agent\n")
    
    test_cases = [
        {
            "question": "Who was Marie Curie?",
            "task_id": "",
            "description": "Wikipedia factual question"
        },
        {
            "question": "What is 25 + 17 * 3?",
            "task_id": "",
            "description": "Math calculation"
        },
        {
            "question": ".rewsna eht sa \"tfel\" drow eht fo etisoppo eht etirw ,ecnetnes siht dnatsrednu uoy fI",
            "task_id": "",
            "description": "Reversed text question"
        },
        {
            "question": "How many continents are there?",
            "task_id": "",
            "description": "General knowledge"
        }
    ]
    
    for i, test_case in enumerate(test_cases, 1):
        print(f"\n{'='*60}")
        print(f"TEST {i}: {test_case['description']}")
        print(f"{'='*60}")
        print(f"Question: {test_case['question']}")
        
        try:
            answer = process_question(test_case["question"], test_case["task_id"])
            print(f"\nAnswer: {answer}")
        except Exception as e:
            print(f"\nTest failed: {str(e)}")
        
        print(f"\n{'-'*60}")
    
    print("\n✅ All tests completed!")

# Initialize Qwen3 with thinking mode disabled
primary_brain = HuggingFaceEndpoint(
    repo_id=primary_model,
    temperature=0.7,
    max_new_tokens=300,
    huggingfacehub_api_token=os.getenv("HF_API_KEY"),
    model_kwargs={"enable_thinking": False, "thinking_prompt": "/no_thinking"}
)