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| import gradio as gr | |
| import os | |
| import time | |
| from groq import Groq | |
| import requests | |
| from bs4 import BeautifulSoup | |
| from urllib.parse import urljoin, urlparse | |
| import re | |
| import json | |
| # API Setup | |
| GROQ_API_KEY = os.getenv("GROQ_API_KEY") | |
| if not GROQ_API_KEY: | |
| raise ValueError("GROQ_API_KEY environment variable is not set.") | |
| client = Groq(api_key=GROQ_API_KEY) | |
| # Helper Functions | |
| def is_valid_url(url): | |
| try: | |
| result = urlparse(url) | |
| return all([result.scheme, result.netloc]) | |
| except ValueError: | |
| return False | |
| def fetch_webpage(url): | |
| try: | |
| response = requests.get(url, timeout=10) | |
| response.raise_for_status() | |
| return response.text | |
| except requests.exceptions.RequestException as e: | |
| return f"Error fetching URL: {e}" | |
| def extract_text_from_html(html): | |
| soup = BeautifulSoup(html, 'html.parser') | |
| text = soup.get_text(separator=' ', strip=True) | |
| return text | |
| # Chat Logic | |
| async def chat_with_agent(user_input, chat_history): | |
| start_time = time.time() | |
| try: | |
| # Prepare chat history | |
| formatted_history = "\n".join([f"User: {msg[0]}\nAI: {msg[1]}" for msg in chat_history[-10:]]) | |
| system_prompt = """You are TaskMaster, an advanced agentic AI designed to help users accomplish their goals through: | |
| 1. Understanding and breaking down complex tasks | |
| 2. Using available tools effectively | |
| 3. Providing creative solutions with occasional humor | |
| 4. Maintaining context and adapting to user needs | |
| Available tools: | |
| - Web scraping (URL required) | |
| - Internet search simulation | |
| You can take actions using: | |
| Action: take_action, Parameters: {"action":"scrape", "url":"https://example.com"} | |
| Action: take_action, Parameters: {"action":"search_internet", "query":"search query"}""" | |
| messages = [ | |
| {"role": "system", "content": system_prompt}, | |
| {"role": "user", "content": user_input} | |
| ] | |
| completion = client.chat.completions.create( | |
| messages=messages, | |
| temperature=0.7, | |
| max_tokens=2048, | |
| stream=True, | |
| stop=None | |
| ) | |
| response = "" | |
| chain_of_thought = "" | |
| tool_execution_count = 0 | |
| for chunk in completion: | |
| if chunk.choices[0].delta and chunk.choices[0].delta.content: | |
| content = chunk.choices[0].delta.content | |
| response += content | |
| if "Chain of Thought:" in content: | |
| chain_of_thought += content.split("Chain of Thought:", 1)[-1] | |
| if "Action:" in content and tool_execution_count < 3: | |
| action_match = re.search(r"Action: (\w+), Parameters: (\{.*\})", content) | |
| if action_match: | |
| tool_execution_count += 1 | |
| action = action_match.group(1) | |
| parameters = json.loads(action_match.group(2)) | |
| if action == "take_action": | |
| if parameters.get("action") == "scrape": | |
| url = parameters.get("url") | |
| if is_valid_url(url): | |
| html_content = fetch_webpage(url) | |
| if not html_content.startswith("Error"): | |
| webpage_text = extract_text_from_html(html_content) | |
| response += f"\nWebpage Content: {webpage_text[:1000]}...\n" | |
| else: | |
| response += f"\nError scraping webpage: {html_content}\n" | |
| else: | |
| response += "\nInvalid URL provided.\n" | |
| elif parameters.get("action") == "search_internet": | |
| query = parameters.get("query") | |
| response += f"\nSearching for: {query}\nSimulated search results would appear here.\n" | |
| compute_time = time.time() - start_time | |
| token_usage = len(user_input.split()) + len(response.split()) | |
| return response, chain_of_thought, f"Compute Time: {compute_time:.2f} seconds", f"Tokens used: {token_usage}" | |
| except Exception as e: | |
| return f"Error: {str(e)}", "", "Error occurred", "" | |
| # Gradio Interface | |
| def create_interface(): | |
| with gr.Blocks(theme=gr.themes.Soft()) as demo: | |
| gr.Markdown("""# 🤖 TaskMaster: Your AI Assistant | |
| Let me help you accomplish your goals through intelligent task execution!""") | |
| with gr.Row(): | |
| with gr.Column(scale=6): | |
| chat_history = gr.Chatbot(label="Chat History", height=600) | |
| with gr.Column(scale=2): | |
| compute_time = gr.Textbox(label="Performance Metrics", interactive=False) | |
| chain_of_thought_display = gr.Textbox(label="Reasoning Process", interactive=False, lines=10) | |
| token_usage_display = gr.Textbox(label="Resource Usage", interactive=False) | |
| user_input = gr.Textbox( | |
| label="Your Request", | |
| placeholder="What would you like me to help you with?", | |
| lines=2 | |
| ) | |
| with gr.Row(): | |
| send_button = gr.Button("Send", variant="primary") | |
| clear_button = gr.Button("Clear") | |
| export_button = gr.Button("Save Chat") | |
| async def handle_chat(chat_history, user_input): | |
| if not user_input.strip(): | |
| return chat_history, "", "", "" | |
| ai_response, chain_of_thought, compute_info, token_usage = await chat_with_agent(user_input, chat_history) | |
| chat_history.append((user_input, ai_response)) | |
| return chat_history, chain_of_thought, compute_info, token_usage | |
| def clear_chat(): | |
| return [], "", "", "" | |
| def export_chat(chat_history): | |
| if not chat_history: | |
| return "No chat history to export.", "" | |
| filename = f"taskmaster_chat_{int(time.time())}.txt" | |
| chat_text = "\n".join([f"User: {item[0]}\nAI: {item[1]}\n" for item in chat_history]) | |
| with open(filename, "w") as file: | |
| file.write(chat_text) | |
| return f"Chat saved to {filename}", "" | |
| # Event handlers | |
| send_button.click(handle_chat, inputs=[chat_history, user_input], outputs=[chat_history, chain_of_thought_display, compute_time, token_usage_display]) | |
| clear_button.click(clear_chat, outputs=[chat_history, chain_of_thought_display, compute_time, token_usage_display]) | |
| export_button.click(export_chat, inputs=[chat_history], outputs=[compute_time, chain_of_thought_display]) | |
| user_input.submit(handle_chat, inputs=[chat_history, user_input], outputs=[chat_history, chain_of_thought_display, compute_time, token_usage_display]) | |
| gr.Markdown("""### 🌟 Capabilities: | |
| - Task Analysis & Breakdown | |
| - Web Information Retrieval | |
| - Creative Problem-Solving | |
| - Context-Aware Responses | |
| - Performance Tracking | |
| - Chat Export | |
| """) | |
| return demo | |
| # Launch the application | |
| if __name__ == "__main__": | |
| demo = create_interface() | |
| demo.launch() |