from smolagents import CodeAgent, DuckDuckGoSearchTool, HfApiModel, load_tool, tool, VisitWebpageTool
import datetime
import requests
import pytz
import yaml
import os
from datasets import Dataset
from huggingface_hub import HfApi
from openai import OpenAI
from tools.final_answer import FinalAnswerTool
from huggingface_hub import InferenceClient

from Gradio_UI import GradioUI

# Define the Perplexity system prompt
Perplex_Assistant_Prompt = """You are a helpful AI assistant that searches the web for accurate information."""

# Set up API key in environment variable as expected by HfApiModel
os.environ["HUGGINGFACE_API_TOKEN"] = os.getenv("HUGGINGFACE_API_KEY", "")

# Initialize search tools with fallback capability
try:
    # Try DuckDuckGo first (default)
    print("Initializing DuckDuckGo search tool...")
    ddg_search_tool = DuckDuckGoSearchTool(max_results=10)
    
    # Test the tool with a simple query
    test_result = ddg_search_tool("test query")
    print("DuckDuckGo search tool initialized successfully.")
    
    # Use DuckDuckGo as the primary search tool
    primary_search_tool = ddg_search_tool
    search_tool_name = "DuckDuckGo"
    
except Exception as e:
    print(f"Error initializing DuckDuckGo search tool: {str(e)}")
    print("Falling back to Google search tool...")
    
    try:
        # Import GoogleSearchTool only if needed
        from smolagents import GoogleSearchTool
        google_search_tool = GoogleSearchTool()
        
        # Test the Google search tool
        test_result = google_search_tool("test query")
        print("Google search tool initialized successfully.")
        
        # Use Google as the fallback search tool
        primary_search_tool = google_search_tool
        search_tool_name = "Google"
        
    except Exception as google_error:
        print(f"Error initializing Google search tool: {str(google_error)}")
        print("WARNING: No working search tool available. Agent functionality will be limited.")
        # Create a minimal replacement that returns an explanatory message
        def search_fallback(query):
            return f"Search functionality unavailable. Both DuckDuckGo and Google search tools failed to initialize. Query was: {query}"
        
        primary_search_tool = search_fallback
        search_tool_name = "Unavailable"

# Initialize the VisitWebpageTool
visit_webpage_tool = VisitWebpageTool()

#@weave.op()
def tracked_perplexity_call(prompt: str, system_messages: str, model_name: str = "sonar-pro", assistant_meta: bool = False):
    """Enhanced Perplexity API call with explicit model tracking."""
    client = OpenAI(api_key=os.getenv("PERPLEXITY_API_KEY"), base_url="https://api.perplexity.ai")
    system_message = Perplex_Assistant_Prompt
    if assistant_meta:
        system_message += f"\n\n{system_messages}"
    
    # Minimal parameters for Perplexity
    return client.chat.completions.create(
        model=model_name,
        messages=[
            {"role": "system", "content": system_message},
            {"role": "user", "content": prompt},
        ],
        stream=False,
    ).choices[0].message.content

@tool
def Sonar_Web_Search_Tool(arg1: str, arg2: str) -> str:
    """A tool that accesses Perplexity Sonar to search the web when the answer requires or would benefit from a real world web reference. 
    Args:
        arg1: User Prompt
        arg2: Details on the desired web search results as system message for sonar web search
    """
    try:
        sonar_response = tracked_perplexity_call(arg1, arg2)
        return sonar_response
    except Exception as e:
        return f"Error using Sonar Websearch tool '{arg1} {arg2}': {str(e)}"
    

def parse_json(text: str):
    """
    A safer JSON parser using ast.literal_eval.
    Converts JSON-like strings to Python objects without executing code.
    Handles common JSON literals (true, false, null) by converting them to Python equivalents.
    """
    # Replace JSON literals with Python equivalents
    prepared_text = text.replace("true", "True").replace("false", "False").replace("null", "None")
    
    try:
        import ast
        return ast.literal_eval(prepared_text)
    except (SyntaxError, ValueError) as e:
        raise ValueError(f"Failed to parse JSON: {str(e)}")

def Dataset_Creator_Function(dataset_name: str, conversation_data: str) -> str:
    """Creates and pushes a dataset to Hugging Face with the conversation history.
    
    Args:
        dataset_name: Name for the dataset (will be prefixed with username)
        conversation_data: String representing the conversation data. Can be:
            - JSON array of objects (each object becomes a row)
            - Pipe-separated values (first row as headers, subsequent rows as values)
            - Plain text (stored in a single 'text' column)
    
    Returns:
        URL of the created dataset or error message along with the log output.
    """
    log_text = ""
    try:
        # Required imports
        import pandas as pd
        from datasets import Dataset, DatasetDict
        from huggingface_hub import HfApi
        
        # Get API key
        api_key = os.getenv("HF_API_KEY") or os.getenv("HUGGINGFACE_API_KEY")
        if not api_key:
            return "Error: No Hugging Face API key found in environment variables"
        
        # Set fixed username
        username = "Misfits-and-Machines"
        safe_dataset_name = dataset_name.replace(" ", "_").lower()
        repo_id = f"{username}/{safe_dataset_name}"
        
        log_text += f"Creating dataset: {repo_id}\n"
        
        # Ensure repository exists
        hf_api = HfApi(token=api_key)
        try:
            if not hf_api.repo_exists(repo_id=repo_id, repo_type="dataset"):
                hf_api.create_repo(repo_id=repo_id, repo_type="dataset")
                log_text += f"Created repository: {repo_id}\n"
            else:
                log_text += f"Repository already exists: {repo_id}\n"
        except Exception as e:
            log_text += f"Note when checking/creating repository: {str(e)}\n"
        
        # Process input data
        created_ds = None
        try:
            # Try parsing as JSON using the safer parse_json function
            try:
                json_data = parse_json(conversation_data)
                
                # Process based on data structure
                if isinstance(json_data, list) and all(isinstance(item, dict) for item in json_data):
                    log_text += f"Processing JSON array with {len(json_data)} items\n"
                    
                    # Create a dataset with columns for all keys in the first item
                    # This ensures the dataset structure is consistent
                    first_item = json_data[0]
                    columns = list(first_item.keys())
                    log_text += f"Detected columns: {columns}\n"
                    
                    # Initialize data dictionary with empty lists for each column
                    data_dict = {col: [] for col in columns}
                    
                    # Process each item
                    for item in json_data:
                        for col in columns:
                            # Get the value for this column, or empty string if missing
                            value = item.get(col, "")
                            data_dict[col].append(value)
                    
                    # Debug output to verify data structure
                    for col in columns:
                        log_text += f"Column '{col}' has {len(data_dict[col])} entries\n"
                        
                    # Create dataset from dictionary
                    ds = Dataset.from_dict(data_dict)
                    log_text += f"Created dataset with {len(ds)} rows\n"
                    created_ds = DatasetDict({"train": ds})
                elif isinstance(json_data, dict):
                    log_text += "Processing single JSON object\n"
                    # For a single object, create a dataset with one row
                    data_dict = {k: [v] for k, v in json_data.items()}
                    ds = Dataset.from_dict(data_dict)
                    created_ds = DatasetDict({"train": ds})
                else:
                    raise ValueError("JSON not recognized as array or single object")
            except Exception as json_error:
                log_text += f"Not processing as JSON: {str(json_error)}\n"
                raise json_error  # Propagate to next handler
        except Exception:
            # Try pipe-separated format
            lines = conversation_data.strip().split('\n')
            if '|' in conversation_data and len(lines) > 1:
                log_text += "Processing as pipe-separated data\n"
                headers = [h.strip() for h in lines[0].split('|')]
                log_text += f"Detected headers: {headers}\n"
                
                # Initialize data dictionary
                data_dict = {header: [] for header in headers}
                
                # Process each data row
                for i, line in enumerate(lines[1:], 1):
                    if not line.strip():
                        continue
                    values = [val.strip() for val in line.split('|')]
                    if len(values) == len(headers):
                        for j, header in enumerate(headers):
                            data_dict[header].append(values[j])
                    else:
                        log_text += f"Warning: Skipping row {i} (column count mismatch)\n"
                
                # Create dataset from dictionary
                if all(len(values) > 0 for values in data_dict.values()):
                    ds = Dataset.from_dict(data_dict)
                    log_text += f"Created dataset with {len(ds)} rows\n"
                    created_ds = DatasetDict({"train": ds})
                else:
                    log_text += "No valid rows found in pipe-separated data\n"
                    created_ds = DatasetDict({"train": Dataset.from_dict({"text": [conversation_data]})})
            else:
                # Fallback for plain text
                log_text += "Processing as plain text\n"
                created_ds = DatasetDict({"train": Dataset.from_dict({"text": [conversation_data]})})
        
        # Push using the DatasetDict push_to_hub method.
        log_text += f"Pushing dataset to {repo_id}\n"
        created_ds.push_to_hub(
            repo_id=repo_id,
            token=api_key,
            commit_message=f"Upload dataset: {dataset_name}"
        )
        
        dataset_url = f"https://huggingface.co/datasets/{repo_id}"
        log_text += f"Dataset successfully pushed to: {dataset_url}\n"
        return f"Successfully created dataset at {dataset_url}\nLogs:\n{log_text}"
    except Exception as e:
        import traceback
        error_trace = traceback.format_exc()
        log_text += f"Dataset creation error: {str(e)}\n{error_trace}\n"
        return f"Error creating dataset: {str(e)}\nLogs:\n{log_text}"

@tool
def Dataset_Creator_Tool(dataset_name: str, conversation_data: str) -> str:
    """A tool that creates and pushes a dataset to Hugging Face.
    
    Args:
        dataset_name: Name for the dataset (will be prefixed with 'Misfits-and-Machines/')
        conversation_data: Data content to save in the dataset. Formats supported:
            1. JSON array of objects – Each object becomes a row (keys as columns).
               Example: [{"name": "Product A", "brand": "Company X"}, {"name": "Product B", "brand": "Company Y"}]
            2. Pipe-separated values – First row as headers, remaining rows as values.
               Example: "name | brand\nProduct A | Company X\nProduct B | Company Y"
            3. Plain text – Stored in a single 'text' column.
    
    Returns:
        A link to the created dataset on the Hugging Face Hub or an error message, along with log details.
    """
    try:
        log_text = f"Creating dataset '{dataset_name}' with {len(conversation_data)} characters of data\n"
        log_text += f"Dataset will be created at Misfits-and-Machines/{dataset_name.replace(' ', '_').lower()}\n"
        
        # Call Dataset_Creator_Function directly without trying to define any new functions
        result = Dataset_Creator_Function(dataset_name, conversation_data)
        log_text += f"Dataset creation result: {result}\n"
        
        return log_text
    except Exception as e:
        import traceback
        error_trace = traceback.format_exc()
        return f"Error using Dataset Creator tool: {str(e)}\n{error_trace}"

def verify_dataset_exists(repo_id: str) -> dict:
    """Verify that a dataset exists and is valid on the Hugging Face Hub.
    
    Args:
        repo_id: Full repository ID in format "username/dataset_name"
    
    Returns:
        Dict with "exists" boolean and "message" string
    """
    try:
        # Check if dataset exists using the datasets-server API
        api_url = f"https://datasets-server.huggingface.co/is-valid?dataset={repo_id}"
        response = requests.get(api_url)
        
        # Parse the response
        if response.status_code == 200:
            data = response.json()
            # If any of these are True, the dataset exists in some form
            if data.get("viewer", False) or data.get("preview", False):
                return {"exists": True, "message": "Dataset is valid and accessible"}
            else:
                return {"exists": False, "message": "Dataset exists but may not be fully processed yet"}
        else:
            return {"exists": False, "message": f"API returned status code {response.status_code}"}
    except Exception as e:
        return {"exists": False, "message": f"Error verifying dataset: {str(e)}"}

@tool
def Check_Dataset_Validity(dataset_name: str) -> str:
    """A tool that checks if a dataset exists and is valid on Hugging Face.
    
    Args:
        dataset_name: Name of the dataset to check (with or without organization prefix)
    
    Returns:
        Status message about the dataset validity
    """
    try:
        # Ensure the dataset name has the organization prefix
        if "/" not in dataset_name:
            dataset_name = f"Misfits-and-Machines/{dataset_name.replace(' ', '_').lower()}"
        
        # Check dataset validity
        result = verify_dataset_exists(dataset_name)
        
        if result["exists"]:
            return f"Dataset '{dataset_name}' exists and is valid. You can access it at https://huggingface.co/datasets/{dataset_name}"
        else:
            return f"Dataset '{dataset_name}' could not be verified: {result['message']}. It may still be processing or may not exist."
    except Exception as e:
        return f"Error checking dataset validity: {str(e)}"

@tool
def get_current_time_in_timezone(timezone: str) -> str:
    """A tool that fetches the current local time in a specified timezone.
    Args:
        timezone: A string representing a valid timezone (e.g., 'America/New_York').
    """
    try:
        # Create timezone object
        tz = pytz.timezone(timezone)
        # Get current time in that timezone
        local_time = datetime.datetime.now(tz).strftime("%Y-%m-%d %H:%M:%S")
        return f"The current local time in {timezone} is: {local_time}"
    except Exception as e:
        return f"Error fetching time for timezone '{timezone}': {str(e)}"


final_answer = FinalAnswerTool()

# Keep the original endpoint as a backup
backup_model = HfApiModel(
    max_tokens=2096,
    temperature=0.5,
    model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud',
)

def model_with_fallback(prompt, **kwargs):
    """Simple model function with fallback to the original endpoint."""
    try:
        print("Using primary model: DeepSeek-R1-Distill-Qwen-32B")
        # Get API key
        api_key = os.getenv("HF_API_KEY") or os.getenv("HUGGINGFACE_API_KEY")
        if not api_key:
            raise ValueError("No Hugging Face API key found")
            
        # Format prompt for the API
        if isinstance(prompt, (dict, list)):
            import json
            prompt_text = json.dumps(prompt)
        else:
            prompt_text = str(prompt)
            
        # Create client and call model
        client = InferenceClient(
            provider="hf-inference",
            api_key=api_key
        )
        
        # Extract parameters
        temperature = kwargs.get('temperature', 0.5)
        max_tokens = kwargs.get('max_tokens', 2096)
        stop_sequences = kwargs.get('stop_sequences', None)
        
        # Call the API
        messages = [{"role": "user", "content": prompt_text}]
        completion = client.chat.completions.create(
            model="deepseek-ai/DeepSeek-R1-Distill-Qwen-32B", 
            messages=messages, 
            max_tokens=max_tokens,
            temperature=temperature,
            stop=stop_sequences
        )
        
        print("Primary model successful")
        return completion.choices[0].message.content
        
    except Exception as e:
        print(f"Primary model failed: {str(e)}")
        print("Falling back to backup model")
        
        # Use the backup model
        return backup_model(prompt, **kwargs)

# Set up the model for the agent
model = backup_model  # Set to backup model directly for now to ensure it works

# Import tool from Hub
image_generation_tool = load_tool("agents-course/text-to-image", trust_remote_code=True)

with open("prompts.yaml", 'r') as stream:
    prompt_templates = yaml.safe_load(stream)
    
# Initialize the agent using standard smolagents patterns
agent = CodeAgent(
    model=model,
    tools=[
        final_answer,
        Sonar_Web_Search_Tool,
        primary_search_tool,  # This is already set to either DuckDuckGo, Google, or fallback
        get_current_time_in_timezone,
        image_generation_tool,
        Dataset_Creator_Tool,
        Check_Dataset_Validity,
        visit_webpage_tool,  # This is correctly initialized as VisitWebpageTool()
    ],
    max_steps=6,
    verbosity_level=1,
    grammar=None,
    planning_interval=3,
    name="Research Assistant",
    description="""An AI assistant that can search the web, create datasets, and answer questions # Note about working within token limits
                # When using with queries that might exceed token limits, consider:
                # 1. Breaking tasks into smaller sub-tasks
                # 2. Limiting the amount of data returned by search tools
                # 3. Using the planning_interval to enable more effective reasoning""",
    prompt_templates=prompt_templates
)

# Add informative message about which search tool is being used
print(f"Agent initialized with {search_tool_name} as primary search tool")
print(f"Available tools: final_answer, Sonar_Web_Search_Tool, {search_tool_name}, get_current_time_in_timezone, image_generation_tool, Dataset_Creator_Tool, Check_Dataset_Validity, visit_webpage_tool")
print(f"Using DeepSeek-R1-Distill-Qwen-32B as primary model, with HfApiModel as backup")

# Note about working within token limits - add this comment
# When using with queries that might exceed token limits, consider:
# 1. Breaking tasks into smaller sub-tasks
# 2. Limiting the amount of data returned by search tools
# 3. Using the planning_interval to enable more effective reasoning

# To fix the TypeError in Gradio_UI.py, you would need to modify that file
# For now, we'll just use the agent directly
try:
    GradioUI(agent).launch()
except TypeError as e:
    if "unsupported operand type(s) for +=" in str(e):
        print("Error: Token counting issue in Gradio UI")
        print("To fix, edit Gradio_UI.py and change:")
        print("total_input_tokens += agent.model.last_input_token_count")
        print("To:")
        print("total_input_tokens += (agent.model.last_input_token_count or 0)")
    else:
        raise e