app.py

import os
import subprocess
import streamlit as st  # For Streamlit
import gradio as gr  # For Gradio
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
import black
from pylint import lint
from io import StringIO
import openai
import sys
from datetime import datetime
import requests
from bs4 import BeautifulSoup
from typing import List, Dict, Optional

from utils.code_utils import (
    refine_code,
    test_code,
    integrate_code,
    CodeRefinementError,
    CodeTestingError,
    CodeIntegrationError,
)

# --- Define custom exceptions for better error handling ---
class InvalidActionError(Exception):
    """Raised when an invalid action is provided."""
    pass

class InvalidInputError(Exception):
    """Raised when invalid input is provided for an action."""
    pass

class CodeGenerationError(Exception):
    """Raised when code generation fails."""
    pass

class AppTestingError(Exception):
    """Raised when app testing fails."""
    pass

class WorkspaceExplorerError(Exception):
    """Raised when workspace exploration fails."""
    pass

class PromptManagementError(Exception):
    """Raised when prompt management fails."""
    pass

class SearchError(Exception):
    """Raised when search fails."""
    pass

# --- Define a class for the AI Agent ---
class AIAgent:
    def __init__(self):
        # --- Initialize tools and attributes ---
        self.tools = {
            "SEARCH": self.search,
            "CODEGEN": self.code_generation,
            "REFINE-CODE": refine_code,  # Use external function
            "TEST-CODE": test_code,  # Use external function
            "INTEGRATE-CODE": integrate_code,  # Use external function
            "TEST-APP": self.test_app,
            "GENERATE-REPORT": self.generate_report,
            "WORKSPACE-EXPLORER": self.workspace_explorer,
            "ADD_PROMPT": self.add_prompt,
            "ACTION_PROMPT": self.action_prompt,
            "COMPRESS_HISTORY_PROMPT": self.compress_history_prompt,
            "LOG_PROMPT": self.log_prompt,
            "LOG_RESPONSE": self.log_response,
            "MODIFY_PROMPT": self.modify_prompt,
            "PREFIX": self.prefix,
            "SEARCH_QUERY": self.search_query,
            "READ_PROMPT": self.read_prompt,
            "TASK_PROMPT": self.task_prompt,
            "UNDERSTAND_TEST_RESULTS_PROMPT": self.understand_test_results_prompt,
        }
        self.task_history: List[Dict[str, str]] = []
        self.current_task: Optional[str] = None
        self.search_engine_url: str = "https://www.google.com/search?q="  # Default search engine
        self.prompts: List[str] = []  # Store prompts for future use
        self.code_generator = pipeline('text-generation', model='gpt2')  # Initialize code generator

    # --- Implement search functionality ---
    def search(self, query: str) -> List[str]:
        """Performs a web search using the specified search engine."""
        search_url = self.search_engine_url + query
        try:
            response = requests.get(search_url)
            response.raise_for_status()  # Raise an exception for bad status codes
            soup = BeautifulSoup(response.content, 'html.parser')
            results = soup.find_all('a', href=True)
            return [result['href'] for result in results]
        except requests.exceptions.RequestException as e:
            raise SearchError(f"Error during search: {e}")

    # --- Implement code generation functionality ---
    def code_generation(self, snippet: str) -> str:
        """Generates code based on the provided snippet."""
        try:
            generated_text = self.code_generator(snippet, max_length=500, num_return_sequences=1)[0]['generated_text']
            return generated_text
        except Exception as e:
            raise CodeGenerationError(f"Error during code generation: {e}")

    # --- Implement app testing functionality ---
    def test_app(self) -> str:
        """Tests the functionality of the app."""
        try:
            subprocess.run(['streamlit', 'run', 'app.py'], check=True)
            return "App tested successfully."
        except subprocess.CalledProcessError as e:
            raise AppTestingError(f"Error during app testing: {e}")

    # --- Implement report generation functionality ---
    def generate_report(self) -> str:
        """Generates a report based on the task history."""
        report = f"## Task Report: {self.current_task}\n\n"
        for task in self.task_history:
            report += f"**Action:** {task['action']}\n"
            report += f"**Input:** {task['input']}\n"
            report += f"**Output:** {task['output']}\n\n"
        return report

    # --- Implement workspace exploration functionality ---
    def workspace_explorer(self) -> str:
        """Provides a workspace explorer functionality."""
        try:
            current_directory = os.getcwd()
            directories = []
            files = []
            for item in os.listdir(current_directory):
                item_path = os.path.join(current_directory, item)
                if os.path.isdir(item_path):
                    directories.append(item)
                elif os.path.isfile(item_path):
                    files.append(item)
            return f"**Directories:** {directories}\n**Files:** {files}"
        except Exception as e:
            raise WorkspaceExplorerError(f"Error during workspace exploration: {e}")

    # --- Implement prompt management functionality ---
    def add_prompt(self, prompt: str) -> str:
        """Adds a new prompt to the agent's knowledge base."""
        try:
            self.prompts.append(prompt)
            return f"Prompt '{prompt}' added successfully."
        except Exception as e:
            raise PromptManagementError(f"Error adding prompt: {e}")

    # --- Implement prompt generation functionality ---
    def action_prompt(self, action: str) -> str:
        """Provides a prompt for a specific action."""
        try:
            if action == "SEARCH":
                return "What do you want to search for?"
            elif action == "CODEGEN":
                return "Provide a code snippet to generate code from."
            elif action == "REFINE-CODE":
                return "Provide the file path of the code to refine."
            elif action == "TEST-CODE":
                return "Provide the file path of the code to test."
            elif action == "INTEGRATE-CODE":
                return "Provide the file path and code snippet to integrate."
            elif action == "TEST-APP":
                return "Test the application."
            elif action == "GENERATE-REPORT":
                return "Generate a report based on the task history."
            elif action == "WORKSPACE-EXPLORER":
                return "Explore the current workspace."
            elif action == "ADD_PROMPT":
                return "Enter the new prompt to add."
            elif action == "ACTION_PROMPT":
                return "Enter the action to get a prompt for."
            elif action == "COMPRESS_HISTORY_PROMPT":
                return "Compress the task history."
            elif action == "LOG_PROMPT":
                return "Enter the event to log."
            elif action == "LOG_RESPONSE":
                return "Log the specified event."
            elif action == "MODIFY_PROMPT":
                return "Enter the prompt to modify."
            elif action == "PREFIX":
                return "Enter the text to add a prefix to."
            elif action == "SEARCH_QUERY":
                return "Enter the topic to generate a search query for."
            elif action == "READ_PROMPT":
                return "Enter the file path to read."
            elif action == "TASK_PROMPT":
                return "Enter the new task to start."
            elif action == "UNDERSTAND_TEST_RESULTS_PROMPT":
                return "Enter your question about the test results."
            else:
                raise InvalidActionError("Please provide a valid action.")
        except InvalidActionError as e:
            raise e

    # --- Implement prompt generation functionality ---
    def compress_history_prompt(self) -> str:
        """Provides a prompt to compress the task history."""
        return "Do you want to compress the task history?"

    # --- Implement prompt generation functionality ---
    def log_prompt(self) -> str:
        """Provides a prompt to log a specific event."""
        return "What event do you want to log?"

    # --- Implement logging functionality ---
    def log_response(self, event: str) -> str:
        """Logs the specified event."""
        print(f"Event logged: {event}")
        return "Event logged successfully."

    # --- Implement prompt modification functionality ---
    def modify_prompt(self, prompt: str) -> str:
        """Modifies an existing prompt."""
        try:
            # Find the prompt to modify
            # Update the prompt
            return f"Prompt '{prompt}' modified successfully."
        except Exception as e:
            raise PromptManagementError(f"Error modifying prompt: {e}")

    # --- Implement prefix functionality ---
    def prefix(self, text: str) -> str:
        """Adds a prefix to the provided text."""
        return f"PREFIX: {text}"

    # --- Implement search query generation functionality ---
    def search_query(self, query: str) -> str:
        """Provides a search query for the specified topic."""
        return f"Search query: {query}"

    # --- Implement file reading functionality ---
    def read_prompt(self, file_path: str) -> str:
        """Provides a prompt to read the contents of a file."""
        try:
            with open(file_path, 'r') as f:
                contents = f.read()
            return contents
        except FileNotFoundError:
            raise InvalidInputError(f"Error: File not found: {file_path}")
        except Exception as e:
            raise InvalidInputError(f"Error reading file: {e}")

    # --- Implement task prompt generation functionality ---
    def task_prompt(self) -> str:
        """Provides a prompt to start a new task."""
        return "What task do you want to start?"

    # --- Implement test results understanding prompt generation functionality ---
    def understand_test_results_prompt(self) -> str:
        """Provides a prompt to understand the test results."""
        return "What do you want to know about the test results?"

    # --- Implement input handling functionality ---
    def handle_input(self, input_str: str):
        """Handles user input and executes the corresponding action."""
        try:
            action, *args = input_str.split()
            if action in self.tools:
                if args:
                    self.task_history.append({
                        "action": action,
                        "input": " ".join(args),
                        "output": self.tools[action](" ".join(args))
                    })
                else:
                    self.task_history.append({
                        "action": action,
                        "input": None,
                        "output": self.tools[action]()
                    })
                print(f"Action: {action}\nInput: {' '.join(args)}\nOutput: {self.tools[action](' '.join(args))}")
            else:
                raise InvalidActionError("Invalid action. Please choose a valid action from the list of tools.")
        except (InvalidActionError, InvalidInputError, CodeGenerationError, 
                CodeRefinementError, CodeTestingError, CodeIntegrationError, 
                AppTestingError, WorkspaceExplorerError, PromptManagementError, 
                SearchError) as e:
            print(f"Error: {e}")

    # --- Implement the main loop of the agent ---
    def run(self):
        """Runs the agent continuously, waiting for user input."""
        while True:
            input_str = input("Enter a command for the AI Agent: ")
            self.handle_input(input_str)

# --- Streamlit Integration ---
if __name__ == '__main__':
    agent = AIAgent()
    st.title("AI Agent")
    st.write("Enter a command for the AI Agent:")
    input_str = st.text_input("")
    agent.handle_input(input_str)
    agent.run()