app.py

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()