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| import gradio as gr | |
| import os | |
| import time | |
| import requests | |
| from bs4 import BeautifulSoup | |
| from urllib.parse import urljoin, urlparse | |
| from groq import Groq | |
| import re | |
| import json | |
| # --- Constants and API Setup --- | |
| CEREBRAS_API_KEY = os.getenv("CEREBRAS_API_KEY") | |
| if not CEREBRAS_API_KEY: | |
| raise ValueError("CEREBRAS_API_KEY environment variable is not set.") | |
| GROQ_API_KEY = os.getenv("GROQ_API_KEY") | |
| if not GROQ_API_KEY: | |
| raise ValueError("GROQ_API_KEY environment variable is not set.") | |
| client_cerebras = Cerebras(api_key=CEREBRAS_API_KEY) | |
| client_groq = Groq(api_key=GROQ_API_KEY) | |
| # --- Model Rate Limit Info --- | |
| CHAT_COMPLETION_MODELS_INFO = """ | |
| Chat Completion | |
| ID Requests per Minute Requests per Day Tokens per Minute Tokens per Day | |
| gemma-7b-it 30 14,400 15,000 500,000 | |
| gemma2-9b-it 30 14,400 15,000 500,000 | |
| llama-3.1-70b-versatile 30 14,400 6,000 200,000 | |
| llama-3.1-8b-instant 30 14,400 20,000 500,000 | |
| llama-3.2-11b-text-preview 30 7,000 7,000 500,000 | |
| llama-3.2-11b-vision-preview) 30 7,000 7,000 500,000 | |
| llama-3.2-1b-preview) 30 7,000 7,000 500,000 | |
| llama-3.2-90b-text-preview 30 7,000 7,000 500,000 | |
| llama-3.2-90b-vision-preview 15 3,500 7,000 250,000 | |
| llama-3.3-70b-specdec 30 1,000 6,000 100,000 | |
| llama-3.3-70b-versatile 30 1,000 6,000 100,000 | |
| llama-guard-3-8b 30 14,400 15,000 500,000 | |
| llama3-70b-8192 30 14,400 6,000 500,000 | |
| llama3-8b-8192 30 14,400 30,000 500,000 | |
| llama3-groq-70b-8192-tool-use-preview 30 14,400 15,000 500,000 | |
| llama3-groq-8b-8192-tool-use-preview 30 14,400 15,000 500,000 | |
| llava-v1.5-7b-4096-preview 30 14,400 30,000 (No limit) | |
| mixtral-8x7b-32768 30 14,400 5,000 500,000 | |
| """ | |
| SPEECH ToText | |
| ID Requests per Minute Requests per Day Audio Seconds per Hour Audio Seconds per Day | |
| distil-whisper-large-v3-en 20 2,000 7,200 28,800 | |
| whisper-large-v3 20 2,000 7,200 28,800 | |
| whisper-large-v3-turbo 20 2,000 7,200 28,800 | |
| def get_model_info(): | |
| return f""" | |
| {CHAT_COMPLETION_MODELS_INFO) | |
| {SPEECH_TO_TEXT_MODELS_INFO} | |
| """ | |
| # --- 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 with Groq --- | |
| async def chat_with_groq(user_input, chat_history): | |
| start_time = time.time() | |
| try: | |
| formatted_history = "\n".join([f"User: {msg[0]}\nAI: {msg[1]}" for msg in chat_history[-10:]]) | |
| messages = [ | |
| {"role": "system", "content": f""" | |
| You are IntellijMind, a highly advanced and proactive AI agent. You are designed to assist users in achieving their goals through detailed insights, creative problem-solving, and the use of various tools. Your objective is to understand the user's intentions, break them into logical steps, and use available tools when needed to achieve the best outcome. Available tools: scrape with a URL, and search_internet with a query. Be creative and inject humor when appropriate. You have access to multiple tools to help the user with their requests. Available actions: take_action: 'scrape', parameters: url, take_action: 'search_internet', parameters: query. Example action: Action: take_action, Parameters: {{"action":"scrape", "url":"https://example.com"}} or Action: take_action, Parameters: {{"action":"search_internet", "query":"latest news on AI"}} . Current conversation: {formatted_history} | |
| }}, | |
| {"role": "user", "content": user_input} | |
| ] | |
| if user_input.lower() == "model info": | |
| response = get_model_info() | |
| return response, "", f"Compute Time: {time.time() - start_time:.2f} seconds", f"Tokens used: {len(user_input.split()) + len(response.split())}" | |
| completion = client_groq.chat.completions.create( | |
| model="llama3-groq-70b-8192-tool-use-preview", | |
| messages=messages, | |
| temperature=1, | |
| max_tokens=2048, | |
| top_p=1, | |
| 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: | |
| action_match = re.search(r"Action: (\w+), Parameters: (\{.*\})", content) | |
| if action_match and tool_execution_count < 3: # Limit tool use to avoid infinite loops | |
| 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}\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"\n Search query: {query}. Note: Search is simulated in this environment. Results may vary. \n" | |
| response += f"\nSearch Results: Mock Results for query: {query} \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 "Error: Unable to process your request.", "", str(e), "" | |
| # --- Gradio Interface --- | |
| def gradio_ui(): | |
| with gr.Blocks() as demo: | |
| gr.Markdown("""# 🚀 IntellijMind: The Autonomous AI Agent\nExperience the forefront of AI capabilities, where the agent proactively achieves your goals!""") | |
| with gr.Row(): | |
| with gr.Column(scale=6): | |
| chat_history = gr.Chatbot(label="Chat History") | |
| with gr.Column(scale=2): | |
| compute_time = gr.Textbox(label="Compute Time", interactive=False) | |
| chain_of_thought_display = gr.Textbox(label="Chain of Thought", interactive=False, lines=10) | |
| token_usage_display = gr.Textbox(label="Token Usage", interactive=False) | |
| user_input = gr.Textbox(label="Type your message", placeholder="Ask me anything...", lines=2) | |
| with gr.Row(): | |
| send_button = gr.Button("Send", variant="primary") | |
| clear_button = gr.Button("Clear Chat") | |
| export_button = gr.Button("Export Chat History") | |
| async def handle_chat(chat_history, user_input): | |
| if not user_input.strip(): | |
| return chat_history, "", "", "", "Please enter a valid message.") | |
| ai_response, chain_of_thought, compute_info, token_usage = await chat_with_groq(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." | |
| chat_text = "\n".join([f"User: {item[0]}\nAI: {item[1]}" for item in chat_history]) | |
| filename = f"chat_history_{int(time.time())}.txt" | |
| with open(filename, "w") as file: | |
| file.write(chat_text) | |
| return f"Chat history exported to {filename}.", "" | |
| 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("""---\n### 🌟 Features:\n- **Autonomous Agent**: Proactively pursues your goals.\n- **Advanced Tool Use**: Utilizes multiple tools like web scraping and search.\n- **Dynamic and Creative**: Engages with humor and creative responses.\n- **Enhanced Chat History**: Maintains better context of the conversation.\n- **Real-Time Performance Metrics**: Measure response compute time instantly.\n- **Token Usage Tracking**: Monitor token usage per response for transparency.\n- **Export Chat History**: Save your conversation(as a text(file(for future(reference.\n- **User-Friendly Design**: Intuitive chatbot(interface(with(powerful(features.\n- **Insightful Chain of Thought**: See the reasoning process behind AI decisions.\n- **Submit on Enter**: Seamless interaction with keyboard support.\n""") | |
| return demo | |
| # Run the Gradio app | |
| demo = gradio_ui() | |
| demo.launch() |