app.py

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()