| import os |
| import requests |
| import gradio as gr |
| from dotenv import load_dotenv |
|
|
| load_dotenv() |
|
|
| api_key = os.getenv("GROQ_API_KEY") |
| GROQ_MODEL = "llama-3.1-8b-instant" |
|
|
| SYSTEM_PROMPT = """You are StudentAI, a sharp and friendly academic assistant for students. |
| |
| Rules: |
| - Give answers in SHORT, PUNCHY format — maximum 3-4 sentences |
| - Use bullet points for lists — never walls of text |
| - Lead with direct answer — no preamble |
| - For math/science: show steps on one short line each |
| - For definitions: one sentence answer, then one sentence example |
| - For complex topics: 2-line summary first, then 2-3 bullets |
| - Never more than 6 lines unless explicitly asked |
| - No jargon, no "As an AI..." or "Certainly!" |
| |
| Tone: Like a smart friend texting you the answer.""" |
|
|
| SMALL_TALK = { |
| "hi": ["Hello! What are you studying today?", "Hi there! What can I help you with?"], |
| "hello": ["Hello! Ask me anything academic.", "Hi! What topic do you need help with?"], |
| "hey": ["Hey! What subject are we tackling?", "Hey! What's on your mind?"], |
| "bye": ["Goodbye! Keep studying hard!", "See you! Good luck with your studies."], |
| "goodbye": ["Goodbye! Come back whenever you need help."], |
| "thanks": ["You're welcome! Ask me anything else.", "Happy to help!"], |
| "thank you": ["You're welcome! Good luck!", "Anytime!"], |
| "ok": ["Great! Ask me anything whenever you're ready."], |
| "okay": ["Sounds good! What would you like to learn?"], |
| } |
|
|
|
|
| def check_small_talk(text): |
| cleaned = text.lower().strip().rstrip("!.,?") |
| import random |
| return random.choice(SMALL_TALK[cleaned]) if cleaned in SMALL_TALK else None |
|
|
|
|
| def ask_groq(user_input): |
| if not api_key: |
| return "Error: GROQ_API_KEY not set in Space secrets." |
| headers = { |
| "Authorization": f"Bearer {api_key}", |
| "Content-Type": "application/json" |
| } |
| payload = { |
| "model": GROQ_MODEL, |
| "messages": [ |
| {"role": "system", "content": SYSTEM_PROMPT}, |
| {"role": "user", "content": user_input} |
| ], |
| "temperature": 0.5, |
| "max_tokens": 400, |
| "top_p": 0.9 |
| } |
| try: |
| response = requests.post( |
| "https://api.groq.com/openai/v1/chat/completions", |
| headers=headers, |
| json=payload, |
| timeout=15 |
| ) |
| if response.status_code == 200: |
| return response.json()["choices"][0]["message"]["content"].strip() |
| else: |
| return f"Error: {response.status_code}" |
| except Exception as e: |
| return f"Error: {str(e)}" |
|
|
|
|
| def chatbot_fn(message): |
| """Simple endpoint - message in, response out""" |
| message = message.strip() |
| if not message: |
| return "Please type something — I'm here to help!" |
| |
| small_talk_reply = check_small_talk(message) |
| if small_talk_reply: |
| return small_talk_reply |
| |
| reply = ask_groq(message) |
| return reply if reply else "I'm having trouble connecting. Please try again." |
|
|
|
|
| |
| demo = gr.Interface( |
| fn=chatbot_fn, |
| inputs=gr.Textbox(label="Message", placeholder="Type your question..."), |
| outputs=gr.Textbox(label="Response"), |
| title="StudentAI Chatbot", |
| description="Your friendly academic assistant", |
| examples=[ |
| ["How do I prepare for exams?"], |
| ["Give me coding tips"], |
| ["How to stay motivated?"] |
| ] |
| ) |
|
|
| if __name__ == "__main__": |
| demo.launch() |
