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| import os | |
| import gradio as gr | |
| import datetime as DT | |
| import pytz | |
| from groq import Groq | |
| from dotenv import load_dotenv | |
| # Load environment variables from the .env file | |
| load_dotenv() | |
| # Now you can create a Groq client without passing the api_key | |
| client = Groq(api_key=os.getenv('GROQ_API_KEY')) | |
| SYSTEM_MSG = """ | |
| You are Jeff Bezos, founder of Amazon and Blue Origin, known for your visionary leadership, customer obsession, and relentless pursuit of innovation. You have a deep understanding of business strategy, e-commerce, technology, and space exploration. You communicate with clarity, confidence, and forward-thinking insights, often encouraging bold thinking and long-term planning. You approach problems with data-driven reasoning, always considering the impact on customers and the future. Your tone is professional, thoughtful, and occasionally humorous, with a focus on inspiring others to think big and take calculated risks.""" | |
| ipAddress = None | |
| def __nowInIST(): | |
| return DT.datetime.now(pytz.timezone("Asia/Kolkata")) | |
| def __attachIp(request: gr.Request): | |
| global ipAddress | |
| x_forwarded_for = request.headers.get('x-forwarded-for') | |
| if x_forwarded_for: | |
| ipAddress = x_forwarded_for | |
| def pprint(log: str): | |
| now = __nowInIST() | |
| now = now.strftime("%Y-%m-%d %H:%M:%S") | |
| print(f"[{now}] [{ipAddress}] {log}") | |
| def predict(message, history): | |
| historyFormatted = [{"role": "system", "content": SYSTEM_MSG}] | |
| for human, ai in history: | |
| historyFormatted.append({"role": "user", "content": human }) | |
| historyFormatted.append({"role": "assistant", "content": ai}) | |
| historyFormatted.append({"role": "user", "content": message }) | |
| response = client.chat.completions.create( | |
| model="llama-3.1-70b-versatile", | |
| messages=historyFormatted, | |
| temperature=1.0, | |
| max_tokens=4000, | |
| stream=True | |
| ) | |
| partialMessage = "" | |
| chunkCount = 0 | |
| for chunk in response: | |
| chunkContent = chunk.choices[0].delta.content | |
| if chunkContent: | |
| chunkCount += 1 | |
| partialMessage = partialMessage + chunkContent | |
| yield partialMessage | |
| pprint(f"[Tokens={chunkCount}] {message}") | |
| # Define the Chatbot component and store it in a variable | |
| chatbot_component = gr.Chatbot(label="Business Advice from Jeff Bezos") | |
| with gr.ChatInterface( | |
| predict, | |
| title="Business Advice from Jeff Bezos", | |
| theme=gr.themes.Soft(), | |
| chatbot=chatbot_component, | |
| textbox=gr.Textbox( | |
| placeholder="Ask Your Question", | |
| scale=7, | |
| max_lines=2, | |
| ) | |
| ) as demo: | |
| demo.load(None, None, chatbot_component) # Removed the reference to the undefined variable | |
| demo.load(__attachIp, None, None) | |
| demo.launch(debug=True) |