app.py

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

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 the standard search tools
ddg_search_tool = DuckDuckGoSearchTool(max_results=10)  # Default is 10 results
# google_search_tool = GoogleSearchTool()

#@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 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 (col1 | col2 | col3) for tabular data
                          - Plain text (stored in a 'text' column)
    
    Returns:
        URL of the created dataset or error message
    """
    try:
        # Required imports
        import json
        import pandas as pd
        from datasets import Dataset
        from huggingface_hub import HfApi
        
        # Get API key from environment variables
        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 for dataset organization
        username = "Misfits-and-Machines"
        
        # Initialize Hugging Face API
        hf_api = HfApi(token=api_key)
        
        # Sanitize dataset name
        safe_dataset_name = dataset_name.replace(" ", "_").lower()
        repo_id = f"{username}/{safe_dataset_name}"
        
        print(f"Creating dataset: {repo_id}")
        
        # First ensure the repository exists
        try:
            repo_exists = hf_api.repo_exists(repo_id=repo_id, repo_type="dataset")
            if not repo_exists:
                hf_api.create_repo(repo_id=repo_id, repo_type="dataset")
                print(f"Created repository: {repo_id}")
            else:
                print(f"Repository already exists: {repo_id}")
        except Exception as e:
            print(f"Note when checking/creating repository: {str(e)}")
        
        # Process the data based on format
        try:
            # Try parsing as JSON first
            json_data = json.loads(conversation_data)
            
            if isinstance(json_data, list) and all(isinstance(item, dict) for item in json_data):
                # Process JSON array of objects (preferred format)
                print(f"Processing JSON array with {len(json_data)} items")
                df = pd.DataFrame(json_data)
                dataset = Dataset.from_pandas(df)
            elif isinstance(json_data, dict):
                # Single JSON object
                print("Processing single JSON object")
                df = pd.DataFrame([json_data])
                dataset = Dataset.from_pandas(df)
            else:
                raise ValueError("JSON format not recognized as array of objects or single object")
        except (json.JSONDecodeError, ValueError) as e:
            # Not valid JSON or not in expected format
            print(f"Not processing as JSON: {str(e)}")
            
            # Check if pipe-separated format
            lines = conversation_data.strip().split('\n')
            if '|' in conversation_data and len(lines) > 1:
                print("Processing as pipe-separated data")
                
                # Parse headers and data rows
                headers = [h.strip() for h in lines[0].split('|')]
                data = []
                
                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):
                        data.append(dict(zip(headers, values)))
                    else:
                        print(f"Warning: Skipping row {i} (column count mismatch)")
                
                if data:
                    df = pd.DataFrame(data)
                    dataset = Dataset.from_pandas(df)
                else:
                    # Fallback to text if no valid rows
                    dataset = Dataset.from_dict({"text": [conversation_data]})
            else:
                # Plain text
                print("Processing as plain text")
                dataset = Dataset.from_dict({"text": [conversation_data]})
        
        # Push to Hugging Face Hub
        print(f"Pushing dataset to {repo_id}")
        dataset.push_to_hub(
            repo_id=repo_id,
            token=api_key,
            split="train"
        )
        
        dataset_url = f"https://huggingface.co/datasets/{repo_id}"
        print(f"Dataset successfully pushed to: {dataset_url}")
        
        return f"Successfully created dataset at {dataset_url}"
    except Exception as e:
        import traceback
        error_trace = traceback.format_exc()
        print(f"Dataset creation error: {str(e)}\n{error_trace}")
        return f"Error creating dataset: {str(e)}\n\nTroubleshooting tips:\n1. Verify your HF_API_KEY is valid\n2. Try a simpler dataset name with only letters and underscores"

@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. Can be formatted in three ways:
            1. JSON array of objects - Each object becomes a row in the dataset with its properties as columns:
               Example: [{"name": "Product A", "brand": "Company X"}, {"name": "Product B", "brand": "Company Y"}]
            2. Pipe-separated values - First row as headers, subsequent rows as values:
               Example: "name | brand\nProduct A | Company X\nProduct B | Company Y"
            3. Plain text - Will be stored in a single 'text' column
    
    Returns:
        Link to the created dataset or error message with troubleshooting steps
    """
    try:
        print(f"Creating dataset '{dataset_name}' with {len(conversation_data)} characters of data")
        print(f"Dataset will be created at Misfits-and-Machines/{dataset_name.replace(' ', '_').lower()}")
        result = Dataset_Creator_Function(dataset_name, conversation_data)
        print(f"Dataset creation result: {result}")
        return result
    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()

# Remove the huggingface_api_key parameter - it's not supported
model = HfApiModel(
    max_tokens=2096,
    temperature=0.5,
    model_id='https://pflgm2locj2t89co.us-east-1.aws.endpoints.huggingface.cloud',  # Using the backup endpoint
    custom_role_conversions=None
)

# 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)
    
agent = CodeAgent(
    model=model,
    tools=[
        final_answer, 
        Sonar_Web_Search_Tool,
        ddg_search_tool,  # Added DuckDuckGo search tool
#        google_search_tool,  # Added Google search tool 
        get_current_time_in_timezone, 
        image_generation_tool,
        Dataset_Creator_Tool,
        Check_Dataset_Validity
    ],  
    max_steps=6,
    verbosity_level=1,
    grammar=None,
    planning_interval=None,
    name=None,
    description=None,
    prompt_templates=prompt_templates
)

# 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