test_llm.py

#!/usr/bin/env python3
"""
Complete LLM Testing Script
Supports Groq and local HuggingFace LLMs with proper LangChain integration.
"""

import os
import sys
from dotenv import load_dotenv

# LangChain & LangGraph imports
try:
    from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
    from langchain_groq import ChatGroq
    from langgraph.graph import START, StateGraph, MessagesState
    from langgraph.prebuilt import ToolNode, tools_condition
    print("✅ LangChain imports successful")
except ImportError as e:
    print(f"❌ Import error: {e}")
    print("💡 Install missing packages: pip install langchain-groq langgraph")
    sys.exit(1)

load_dotenv()


class LocalHuggingFaceLLM:
    """Custom wrapper for local HuggingFace models"""
    def __init__(self, model, tokenizer, device):
        self.model = model
        self.tokenizer = tokenizer
        self.device = device
        self.model.eval()

    def invoke(self, messages):
        """Generate response from local model, return AIMessage"""
        from langchain_core.messages import AIMessage
        import torch

        # Convert messages to text
        if isinstance(messages, list):
            text = ""
            for msg in messages:
                if hasattr(msg, 'content'):
                    if hasattr(msg, 'type'):
                        if msg.type == "system":
                            text += f"System: {msg.content}\n"
                        elif msg.type == "human":
                            text += f"Human: {msg.content}\n"
                        else:
                            text += f"{msg.content}\n"
                    else:
                        text += f"Human: {msg.content}\n"
                else:
                    text += str(msg) + "\n"
            text += "Assistant:"
        else:
            text = str(messages)

        try:
            inputs = self.tokenizer.encode(text, return_tensors="pt", max_length=512, truncation=True)
            if self.device == "cuda" and torch.cuda.is_available():
                inputs = inputs.to(self.device)
                self.model = self.model.to(self.device)

            outputs = self.model.generate(
                inputs,
                max_new_tokens=100,
                do_sample=True,
                temperature=0.7,
                pad_token_id=self.tokenizer.eos_token_id,
                attention_mask=torch.ones_like(inputs),
                no_repeat_ngram_size=2,
                early_stopping=True
            )

            response_text = self.tokenizer.decode(outputs[0][len(inputs[0]):], skip_special_tokens=True).strip()
            return AIMessage(content=response_text if response_text else "I understand.")

        except Exception as e:
            return AIMessage(content=f"Error generating response: {str(e)}")


def create_local_huggingface_llm():
    """Initialize local HuggingFace model"""
    try:
        from transformers import AutoTokenizer, AutoModelForCausalLM
        import torch

        model_name = "microsoft/DialoGPT-small"
        device = "cuda" if torch.cuda.is_available() else "cpu"

        tokenizer = AutoTokenizer.from_pretrained(model_name, padding_side="left")
        if tokenizer.pad_token is None:
            tokenizer.pad_token = tokenizer.eos_token

        model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16 if device == "cuda" else torch.float32)
        return LocalHuggingFaceLLM(model, tokenizer, device)

    except Exception as e:
        print(f"❌ Failed to load local HuggingFace model: {e}")
        return None


def create_minimal_graph(provider: str = "groq"):
    """Create a minimal graph for testing"""
    try:
        if provider == "groq":
            if not os.getenv("GROQ_API_KEY"):
                raise ValueError("GROQ_API_KEY not found")
            llm = ChatGroq(model="qwen/qwen3-32b", temperature=0)

            def assistant(state: MessagesState):
                return {"messages": [llm.invoke(state["messages"])]}

            builder = StateGraph(MessagesState)
            builder.add_node("assistant", assistant)
            builder.add_edge(START, "assistant")
            return builder.compile()

        elif provider == "huggingface_local":
            llm = create_local_huggingface_llm()
            if llm is None:
                raise ValueError("Failed to create local HuggingFace model")

            def assistant(state: MessagesState):
                # Return AIMessage directly
                return {"messages": [llm.invoke(state["messages"])]}

            builder = StateGraph(MessagesState)
            builder.add_node("assistant", assistant)
            builder.add_edge(START, "assistant")
            return builder.compile()

        else:
            raise ValueError(f"Unknown provider: {provider}")

    except Exception as e:
        print(f"❌ Failed to create minimal graph: {e}")
        return None


def test_basic_llm_response(provider: str = "groq"):
    """Test basic LLM response"""
    print(f"\n🧪 Testing Basic LLM Response ({provider})")
    try:
        if provider == "groq":
            if not os.getenv("GROQ_API_KEY"):
                return {"status": "error", "error": "GROQ_API_KEY not found"}
            llm = ChatGroq(model="qwen/qwen3-32b", temperature=0)
        elif provider == "huggingface_local":
            llm = create_local_huggingface_llm()
            if llm is None:
                return {"status": "error", "error": "Failed to create local HuggingFace model"}
        else:
            return {"status": "error", "error": f"Unknown provider: {provider}"}

        test_message = "Hello! Please respond with 'LLM is working correctly'"
        response = llm.invoke([HumanMessage(content=test_message)])
        print(f"📥 Response: {response.content[:200]}")
        return {"status": "success", "provider": provider, "response": response.content}

    except Exception as e:
        return {"status": "error", "error": str(e)}


def test_llm_with_system_prompt(provider: str = "groq"):
    """Test LLM with system prompt"""
    print(f"\n🧪 Testing LLM with System Prompt ({provider})")
    try:
        if provider == "groq":
            llm = ChatGroq(model="qwen/qwen3-32b", temperature=0)
        elif provider == "huggingface_local":
            llm = create_local_huggingface_llm()
            if llm is None:
                return {"status": "error", "error": "Failed to create local HuggingFace model"}
        else:
            return {"status": "error", "error": f"Unknown provider: {provider}"}

        system_msg = SystemMessage(content="You are a helpful assistant. Answer briefly and clearly.")
        user_msg = HumanMessage(content="What is 2+2? Just give me the number.")

        response = llm.invoke([system_msg, user_msg])
        print(f"📥 Response: {response.content}")
        return {"status": "success", "provider": provider, "response": response.content}

    except Exception as e:
        return {"status": "error", "error": str(e)}


def test_graph_workflow(provider: str = "groq"):
    """Test graph workflow"""
    print(f"\n🧪 Testing Graph Workflow ({provider})")
    try:
        graph = create_minimal_graph(provider)
        if graph is None:
            return {"status": "error", "error": "Failed to create graph"}

        test_query = "What is 5 + 3? Just give me the answer."
        result = graph.invoke({"messages": [HumanMessage(content=test_query)]})

        if result and "messages" in result:
            last_message = result["messages"][-1]
            print(f"📥 Final response: {last_message.content}")
            return {"status": "success", "response": last_message.content, "message_count": len(result["messages"])}
        else:
            return {"status": "error", "error": "No valid response from graph"}

    except Exception as e:
        return {"status": "error", "error": str(e)}


def run_all_tests():
    """Run all LLM tests"""
    results = {}
    # Groq tests
    results["groq_basic"] = test_basic_llm_response("groq")
    results["groq_system_prompt"] = test_llm_with_system_prompt("groq")
    results["groq_graph"] = test_graph_workflow("groq")
    # HuggingFace local tests
    results["huggingface_local_basic"] = test_basic_llm_response("huggingface_local")
    results["huggingface_local_system_prompt"] = test_llm_with_system_prompt("huggingface_local")
    results["huggingface_local_graph"] = test_graph_workflow("huggingface_local")
    return results


if __name__ == "__main__":
    test_results = run_all_tests()
    print("\n📊 Test Results:")
    for k, v in test_results.items():
        print(f"{k}: {v}")