import os
import gradio as gr
import requests
import inspect
import pandas as pd
from typing import Dict, Any, List, TypedDict
from langgraph.graph import Graph, StateGraph
from langgraph.prebuilt import ToolNode
from pydantic import BaseModel, Field
from tools import create_calculator_tool, create_search_tool
print("trial")
# (Keep Constants as is)
# --- Constants ---
DEFAULT_API_URL = "https://agents-course-unit4-scoring.hf.space"
MODEL_API_URL = "https://api-inference.huggingface.co/models/Qwen/Qwen2.5-Coder-32B-Instruct"
HF_TOKEN = os.getenv("HF_TOKEN")  # Make sure to set this environment variable

class AgentState(BaseModel):
    """Schema for the agent's state."""
    question: str = Field(..., description="The original question")
    current_step: str = Field(default="analyze", description="Current step in the workflow")
    tool_output: str = Field(default="", description="Output from the last tool used")
    final_answer: str = Field(default="", description="The final answer to be returned")
    history: List[Dict[str, str]] = Field(default_factory=list, description="History of operations performed")
    needs_more_info: bool = Field(default=False, description="Whether more information is needed")
    search_query: str = Field(default="", description="Current search query if any")

class BasicAgent:
    def __init__(self):
        print("Initializing BasicAgent with Qwen2.5-Coder-32B-Instruct API...")
        if not HF_TOKEN:
            raise ValueError("HF_TOKEN environment variable not set. Please set your Hugging Face API token.")
        
        # Initialize tools
        print("In constructor of basic agent")
        self.calculator = create_calculator_tool()
        print("Calculator tool created")
        self.search_tool = create_search_tool()
        print("Search tool created")
        # Set up headers for API calls
        self.headers = {
            "Authorization": f"Bearer {HF_TOKEN}",
            "Content-Type": "application/json"
        }
        
        # Create the agent workflow
        print("Creating workflow variable")
        self.workflow = self._create_workflow()
        print("BasicAgent initialization complete.")

    def _create_workflow(self) -> Graph:
        """Create the agent workflow using LangGraph."""
        # Create the workflow with state schema
        print("Creating Stategraph : error happens here?")
        workflow = StateGraph(state_schema= AgentState)
        print("Stategraph created")
        # Add nodes
        workflow.add_node("analyze", self._analyze_question)
        workflow.add_node("calculator", self._use_calculator)
        workflow.add_node("search", self._use_search)
        workflow.add_node("final_answer", self._generate_final_answer)
        
        # Define edges
        workflow.add_edge("analyze", "calculator")
        workflow.add_edge("analyze", "search")
        workflow.add_edge("analyze", "final_answer")
        workflow.add_edge("calculator", "final_answer")
        workflow.add_edge("search", "final_answer")
        
        # Define conditional edges
        def router(state: AgentState) -> str:
            if state.current_step == 'calculator':
                return 'calculator'
            elif state.current_step == 'search':
                return 'search'
            elif state.current_step == 'final_answer':
                return 'final_answer'
            return 'analyze'
        
        workflow.add_conditional_edges(
            "analyze",
            router,
            {
                "calculator": "calculator",
                "search": "search",
                "final_answer": "final_answer"
            }
        )
        
        # Set entry and exit points
        workflow.set_entry_point("analyze")
        workflow.set_finish_point("final_answer")
        
        return workflow.compile()

    def _call_llm_api(self, prompt: str) -> str:
        """Call the Qwen model through the Hugging Face API."""
        try:
            response = requests.post(
                MODEL_API_URL,
                headers=self.headers,
                json={"inputs": prompt, "parameters": {"max_length": 200}}
            )
            response.raise_for_status()
            return response.json()[0]["generated_text"]
        except Exception as e:
            print(f"Error calling LLM API: {e}")
            return f"Error getting response from LLM: {str(e)}"

    def _analyze_question(self, state: AgentState) -> AgentState:
        """Analyze the question and determine the next step."""
        prompt = f"""Analyze this question and determine what needs to be done: {state.question}
        Return your analysis in this format:
        {{
            "needs_calculation": true/false,
            "needs_search": true/false,
            "search_query": "query if needed",
            "calculation": {{
                "operation": "add/subtract/multiply/divide",
                "numbers": [numbers if needed]
            }}
        }}
        """
        
        analysis = eval(self._call_llm_api(prompt))
        state.needs_more_info = analysis.get('needs_search', False)
        state.search_query = analysis.get('search_query', '')
        
        if analysis.get('needs_calculation', False):
            state.current_step = 'calculator'
            state.tool_output = str(analysis['calculation'])
        elif analysis.get('needs_search', False):
            state.current_step = 'search'
        else:
            state.current_step = 'final_answer'
        
        return state

    def _use_calculator(self, state: AgentState) -> AgentState:
        """Use the calculator tool."""
        try:
            result = self.calculator.invoke({"input": eval(state.tool_output)})
            state.history.append({
                'step': 'calculator',
                'input': state.tool_output,
                'output': str(result['output'].result)
            })
            state.current_step = 'final_answer'
        except Exception as e:
            state.history.append({
                'step': 'calculator_error',
                'error': str(e)
            })
            state.current_step = 'final_answer'
        return state

    def _use_search(self, state: AgentState) -> AgentState:
        """Use the search tool."""
        try:
            result = self.search_tool.invoke({
                "input": {
                    "query": state.search_query,
                    "max_results": 3
                }
            })
            state.history.append({
                'step': 'search',
                'query': state.search_query,
                'results': [str(r) for r in result['output'].results]
            })
            state.needs_more_info = False
            state.current_step = 'final_answer'
        except Exception as e:
            state.history.append({
                'step': 'search_error',
                'error': str(e)
            })
            state.current_step = 'final_answer'
        return state

    def _generate_final_answer(self, state: AgentState) -> AgentState:
        """Generate the final answer based on all gathered information."""
        history_str = "\n".join([f"{h['step']}: {h.get('output', h.get('results', h.get('error', '')))}" 
                               for h in state.history])
        
        prompt = f"""Based on the following information and history, provide a final answer to the question: {state.question}
        
        History of steps taken:
        {history_str}
        
        Provide a clear, concise answer that addresses the original question.
        """
        
        state.final_answer = self._call_llm_api(prompt)
        return state

    def __call__(self, question: str) -> str:
        """Process a question through the agent workflow."""
        print(f"Agent received question: {question[:50]}...")
        
        try:
            # Initialize the state
            initial_state = AgentState(
                question=question,
                current_step="analyze",
                tool_output="",
                final_answer="",
                history=[],
                needs_more_info=False,
                search_query=""
            )
            
            # Run the workflow
            final_state = self.workflow.invoke(initial_state)
            return final_state.final_answer
            
        except Exception as e:
            print(f"Error in agent processing: {e}")
            return f"I encountered an error while processing your question: {str(e)}"

def run_and_submit_all(profile: gr.OAuthProfile | None):
    """
    Fetches all questions, runs the BasicAgent on them, submits all answers,
    and displays the results.
    """
    # --- Determine HF Space Runtime URL and Repo URL ---
    space_id = os.getenv("SPACE_ID") # Get the SPACE_ID for sending link to the code
    print("Space ID: ", space_id)
    if profile:
        username = f"{profile.username}"
        print(f"User logged in: {username}")
    else:
        print("User not logged in.")
        return "Please Login to Hugging Face with the button.", None

    api_url = DEFAULT_API_URL
    questions_url = f"{api_url}/questions"
    submit_url = f"{api_url}/submit"

    # 1. Instantiate Agent
    try:
        print("Initializing agent: trial ")
        agent = BasicAgent()
        print("Agent initialized successfully with workflow.")
    except Exception as e:
        print(f"Error instantiating agent: {e}")
        return f"Error initializing agent: {e}", None

    # In the case of an app running as a hugging Face space, this link points toward your codebase
    agent_code = f"https://huggingface.co/spaces/{space_id}/tree/main"
    print(f"Agent code location: {agent_code}")

    # 2. Fetch Questions
    print(f"Fetching questions from: {questions_url}")
    try:
        response = requests.get(questions_url, timeout=15)
        response.raise_for_status()
        questions_data = response.json()
        if not questions_data:
            print("Fetched questions list is empty.")
            return "Fetched questions list is empty or invalid format.", None
        print(f"Fetched {len(questions_data)} questions.")
    except requests.exceptions.RequestException as e:
        print(f"Error fetching questions: {e}")
        return f"Error fetching questions: {e}", None
    except requests.exceptions.JSONDecodeError as e:
        print(f"Error decoding JSON response from questions endpoint: {e}")
        print(f"Response text: {response.text[:500]}")
        return f"Error decoding server response for questions: {e}", None
    except Exception as e:
        print(f"An unexpected error occurred fetching questions: {e}")
        return f"An unexpected error occurred fetching questions: {e}", None

    # 3. Run your Agent
    results_log = []
    answers_payload = []
    print(f"Running agent workflow on {len(questions_data)} questions...")
    
    for item in questions_data:
        task_id = item.get("task_id")
        question_text = item.get("question")
        if not task_id or question_text is None:
            print(f"Skipping item with missing task_id or question: {item}")
            continue

        try:
            # Initialize the state for this question
            initial_state = AgentState(
                question=question_text,
                current_step="analyze",
                tool_output="",
                final_answer="",
                history=[],
                needs_more_info=False,
                search_query=""
            )
            
            # Run the workflow for this question
            print(f"\nProcessing question {task_id}: {question_text[:50]}...")
            final_state = agent.workflow.invoke(initial_state)
            
            # Log the workflow history
            workflow_history = "\n".join([
                f"Step: {h['step']}\n" +
                f"Input: {h.get('input', h.get('query', ''))}\n" +
                f"Output: {h.get('output', h.get('results', h.get('error', '')))}"
                for h in final_state.history
            ])
            
            # Add to results
            submitted_answer = final_state.final_answer
            answers_payload.append({"task_id": task_id, "submitted_answer": submitted_answer})
            results_log.append({
                "Task ID": task_id,
                "Question": question_text,
                "Submitted Answer": submitted_answer,
                "Workflow History": workflow_history
            })
            
            print(f"Completed question {task_id} with {len(final_state.history)} workflow steps")
            
        except Exception as e:
            print(f"Error running agent workflow on task {task_id}: {e}")
            results_log.append({
                "Task ID": task_id,
                "Question": question_text,
                "Submitted Answer": f"WORKFLOW ERROR: {e}",
                "Workflow History": "Error occurred before workflow completion"
            })

    if not answers_payload:
        print("Agent did not produce any answers to submit.")
        return "Agent did not produce any answers to submit.", pd.DataFrame(results_log)

    # 4. Prepare Submission 
    submission_data = {
        "username": username.strip(),
        "agent_code": agent_code,
        "answers": answers_payload
    }
    status_update = f"Agent workflow finished. Submitting {len(answers_payload)} answers for user '{username}'..."
    print(status_update)

    # 5. Submit
    print(f"Submitting {len(answers_payload)} answers to: {submit_url}")
    try:
        response = requests.post(submit_url, json=submission_data, timeout=60)
        response.raise_for_status()
        result_data = response.json()
        final_status = (
            f"Submission Successful!\n"
            f"User: {result_data.get('username')}\n"
            f"Overall Score: {result_data.get('score', 'N/A')}% "
            f"({result_data.get('correct_count', '?')}/{result_data.get('total_attempted', '?')} correct)\n"
            f"Message: {result_data.get('message', 'No message received.')}"
        )
        print("Submission successful.")
        results_df = pd.DataFrame(results_log)
        return final_status, results_df
    except requests.exceptions.HTTPError as e:
        error_detail = f"Server responded with status {e.response.status_code}."
        try:
            error_json = e.response.json()
            error_detail += f" Detail: {error_json.get('detail', e.response.text)}"
        except requests.exceptions.JSONDecodeError:
            error_detail += f" Response: {e.response.text[:500]}"
        status_message = f"Submission Failed: {error_detail}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except requests.exceptions.Timeout:
        status_message = "Submission Failed: The request timed out."
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except requests.exceptions.RequestException as e:
        status_message = f"Submission Failed: Network error - {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df
    except Exception as e:
        status_message = f"An unexpected error occurred during submission: {e}"
        print(status_message)
        results_df = pd.DataFrame(results_log)
        return status_message, results_df


# --- Build Gradio Interface using Blocks ---
with gr.Blocks() as demo:
    gr.Markdown("# Basic Agent Evaluation Runner")
    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).
        This space provides a basic setup and is intentionally sub-optimal to encourage you to develop your own, more robust solution. For instance for the delay process of the submit button, a solution could be to cache the answers and submit in a seperate action or even to answer the questions in async.
        """
    )

    gr.LoginButton()

    run_button = gr.Button("Run Evaluation & Submit All Answers")

    status_output = gr.Textbox(label="Run Status / Submission Result", lines=5, interactive=False)
    # Removed max_rows=10 from DataFrame constructor
    results_table = gr.DataFrame(label="Questions and Agent Answers", wrap=True)

    run_button.click(
        fn=run_and_submit_all,
        outputs=[status_output, results_table]
    )

if __name__ == "__main__":
    print("\n" + "-"*30 + " App Starting " + "-"*30)
    # Check for SPACE_HOST and SPACE_ID at startup for information
    space_host_startup = os.getenv("SPACE_HOST")
    space_id_startup = os.getenv("SPACE_ID") # Get SPACE_ID at startup

    if space_host_startup:
        print(f"✅ SPACE_HOST found: {space_host_startup}")
        print(f"   Runtime URL should be: https://{space_host_startup}.hf.space")
    else:
        print("ℹ️  SPACE_HOST environment variable not found (running locally?).")

    if space_id_startup: # Print repo URLs if SPACE_ID is found
        print(f"✅ SPACE_ID found: {space_id_startup}")
        print(f"   Repo URL: https://huggingface.co/spaces/{space_id_startup}")
        print(f"   Repo Tree URL: https://huggingface.co/spaces/{space_id_startup}/tree/main")
    else:
        print("ℹ️  SPACE_ID environment variable not found (running locally?). Repo URL cannot be determined.")

    print("-"*(60 + len(" App Starting ")) + "\n")

    print("Launching Gradio Interface for Basic Agent Evaluation...")
    demo.launch(debug=True, share=False)