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| import os | |
| from dotenv import load_dotenv | |
| load_dotenv() # Load environment variables from .env file | |
| import gradio as gr | |
| from threading import Thread | |
| import tiktoken | |
| import logging | |
| from src.config import Config | |
| from src.logger import Logger | |
| from src.project import ProjectManager | |
| from src.state import AgentState | |
| from src.agents import Agent | |
| # Initialize core components | |
| manager = ProjectManager() | |
| AgentState = AgentState() | |
| config = Config() | |
| logger = Logger() | |
| TIKTOKEN_ENC = tiktoken.get_encoding("cl100k_base") | |
| # Configure logging | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| def process_message(message, base_model="gpt-3.5-turbo", project_name="default", search_engine="duckduckgo"): | |
| try: | |
| agent = Agent(base_model=base_model, search_engine=search_engine.lower()) | |
| state = AgentState.get_latest_state(project_name) | |
| if not state: | |
| agent.execute(message, project_name) | |
| else: | |
| if AgentState.is_agent_completed(project_name): | |
| agent.subsequent_execute(message, project_name) | |
| else: | |
| agent.execute(message, project_name) | |
| # Get the latest messages | |
| messages = manager.get_messages(project_name) | |
| return messages[-1]["message"] if messages else "No response generated" | |
| except Exception as e: | |
| logger.error(f"Error processing message: {str(e)}") | |
| return f"An error occurred: {str(e)}" | |
| def create_gradio_interface(): | |
| with gr.Blocks(title="Devika AI Assistant", theme=gr.themes.Soft()) as interface: | |
| gr.Markdown(""" | |
| # 🤖 Devika AI Assistant | |
| Devika is an advanced AI coding assistant that helps you with: | |
| - Writing and debugging code | |
| - Creating new projects | |
| - Answering programming questions | |
| - And much more! | |
| Simply type your request below and Devika will help you out. | |
| """) | |
| with gr.Row(): | |
| with gr.Column(scale=2): | |
| message_input = gr.Textbox( | |
| label="Your Message", | |
| placeholder="Type your coding request here...", | |
| lines=3 | |
| ) | |
| with gr.Row(): | |
| model_dropdown = gr.Dropdown( | |
| choices=["gpt-3.5-turbo", "gpt-4", "claude-3-opus"], | |
| value="gpt-3.5-turbo", | |
| label="Model" | |
| ) | |
| search_engine_dropdown = gr.Dropdown( | |
| choices=["DuckDuckGo", "Bing", "Google"], | |
| value="DuckDuckGo", | |
| label="Search Engine" | |
| ) | |
| submit_btn = gr.Button("Send Message", variant="primary") | |
| with gr.Column(scale=3): | |
| output_box = gr.Markdown(label="Devika's Response") | |
| # Add examples | |
| gr.Examples( | |
| examples=[ | |
| ["Create a React component for a todo list", "gpt-3.5-turbo", "DuckDuckGo"], | |
| ["Help me understand how to use Python decorators", "gpt-3.5-turbo", "DuckDuckGo"], | |
| ["Write a Node.js API endpoint for user authentication", "gpt-3.5-turbo", "DuckDuckGo"] | |
| ], | |
| inputs=[message_input, model_dropdown, search_engine_dropdown], | |
| outputs=output_box, | |
| fn=lambda x, y, z: process_message(x, y, "default", z), | |
| cache_examples=True | |
| ) | |
| submit_btn.click( | |
| fn=process_message, | |
| inputs=[message_input, model_dropdown, gr.Textbox(value="default", visible=False), search_engine_dropdown], | |
| outputs=output_box | |
| ) | |
| return interface | |
| # Create and launch the Gradio interface | |
| interface = create_gradio_interface() | |
| if __name__ == "__main__": | |
| interface.launch( | |
| server_name="0.0.0.0", | |
| server_port=7860, | |
| share=False, | |
| debug=False | |
| ) |