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| import gradio as gr | |
| import os | |
| import time | |
| from cerebras.cloud.sdk import Cerebras | |
| import requests | |
| from bs4 import BeautifulSoup | |
| from urllib.parse import urljoin, urlparse | |
| from groq import Groq | |
| import asyncio | |
| 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.") | |
| client_cerebras = Cerebras(api_key=CEREBRAS_API_KEY) | |
| client_groq = Groq() | |
| # --- Rate Limiting --- | |
| CEREBRAS_REQUESTS_PER_MINUTE = 30 | |
| CEREBRAS_TOKENS_PER_MINUTE = 6000 # using lowest token limit for versatile model | |
| GROQ_REQUESTS_PER_MINUTE = 30 | |
| GROQ_TOKENS_PER_MINUTE = 15000 # using token limit for tool-use-preview model | |
| cerebras_request_queue = asyncio.Queue() | |
| groq_request_queue = asyncio.Queue() | |
| last_cerebras_request_time = 0 | |
| last_groq_request_time = 0 | |
| cerebras_token_count = 0 | |
| groq_token_count = 0 | |
| # --- 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-3.2-3b-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_TO_TEXT_MODELS_INFO = """ | |
| Speech To Text | |
| 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() # Raise an exception for bad status codes | |
| 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 | |
| # --- Asynchronous Rate Limit Logic --- | |
| async def check_cerebras_rate_limit(num_tokens): | |
| global last_cerebras_request_time | |
| global cerebras_token_count | |
| current_time = time.time() | |
| elapsed_time = current_time - last_cerebras_request_time | |
| if elapsed_time < 60 and cerebras_request_queue.qsize() >= CEREBRAS_REQUESTS_PER_MINUTE: | |
| await asyncio.sleep(60-elapsed_time) | |
| if elapsed_time < 60 and (cerebras_token_count + num_tokens) > CEREBRAS_TOKENS_PER_MINUTE : | |
| time_to_wait = 60 - elapsed_time | |
| await asyncio.sleep(time_to_wait) | |
| cerebras_request_queue.put_nowait(current_time) | |
| last_cerebras_request_time = time.time() | |
| cerebras_token_count = num_tokens if (elapsed_time > 60) else (cerebras_token_count + num_tokens) | |
| async def check_groq_rate_limit(num_tokens): | |
| global last_groq_request_time | |
| global groq_token_count | |
| current_time = time.time() | |
| elapsed_time = current_time - last_groq_request_time | |
| if elapsed_time < 60 and groq_request_queue.qsize() >= GROQ_REQUESTS_PER_MINUTE: | |
| await asyncio.sleep(60 - elapsed_time) | |
| if elapsed_time < 60 and (groq_token_count + num_tokens) > GROQ_TOKENS_PER_MINUTE : | |
| time_to_wait = 60 - elapsed_time | |
| await asyncio.sleep(time_to_wait) | |
| groq_request_queue.put_nowait(current_time) | |
| last_groq_request_time = time.time() | |
| groq_token_count = num_tokens if (elapsed_time > 60) else (groq_token_count + num_tokens) | |
| # --- Chat Logic with Groq --- | |
| async def chat_with_groq(user_input, chat_history): | |
| start_time = time.time() | |
| try: | |
| # Prepare chat history for the prompt | |
| formatted_history = "\n".join([f"User: {msg[0]}\nAI: {msg[1]}" for msg in chat_history[-5:]]) | |
| # Check for web scraping command | |
| if user_input.lower().startswith("scrape"): | |
| parts = user_input.split(maxsplit=1) | |
| if len(parts) > 1: | |
| url = parts[1].strip() | |
| if is_valid_url(url): | |
| html_content = fetch_webpage(url) | |
| if not html_content.startswith("Error"): | |
| webpage_text = extract_text_from_html(html_content) | |
| user_input = f"The content from the webpage: {webpage_text}. {user_input}" | |
| else: | |
| user_input = f"{html_content}. {user_input}" | |
| else: | |
| user_input = "Invalid URL provided. " + user_input | |
| messages = [ | |
| {"role": "system", "content": f"""You are IntellijMind, an advanced AI designed to assist users with detailed insights, problem-solving, and chain-of-thought reasoning. You have access to various tools to help the user, and can initiate actions when needed. Be creative and inject humor when appropriate. You can use tools to browse the web when instructed with a 'scrape' command followed by a URL. If there is a request for model info, use the get_model_info function. Current conversation: {formatted_history} Available actions: take_action: 'scrape', parameters: url. Example action: Action: take_action, Parameters: {{"action":"scrape", "url":"https://example.com"}} """}, | |
| {"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())}" | |
| num_tokens = len(user_input.split()) | |
| await check_groq_rate_limit(num_tokens) | |
| completion = client_groq.chat.completions.create( | |
| model="llama3-groq-70b-8192-tool-use-preview", | |
| messages=messages, | |
| temperature=1, | |
| max_tokens=1024, | |
| top_p=1, | |
| stream=True, | |
| stop=None, | |
| ) | |
| response = "" | |
| chain_of_thought = "" | |
| 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] | |
| # Check if action needs to be taken | |
| if "Action:" in content: | |
| action_match = re.search(r"Action: (\w+), Parameters: (\{.*\})", content) | |
| if action_match: | |
| 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" | |
| 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 Crazy Agent Chatbot\nExperience the most advanced chatbot for deep insights, chain-of-thought reasoning, and unmatched clarity! Get ready for some proactive action!""") | |
| 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- **Advanced Reasoning**: Chain-of-thought explanations for complex queries.\n- **Proactive Actions**: The agent will take actions without being explicitly asked.\n- **Web Scraping**: The agent will use the scrape command if needed\n- **Humor and Creativity**: Enjoy a more engaging and creative experience.\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() |