app.py

import gradio as gr
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch

print("🔄 Loading CCISD TEKS Educational Assistant...")
print("   This may take 1-2 minutes on first launch...")

# Load base model + LoRA adapter
BASE_MODEL = "mistralai/Mistral-7B-Instruct-v0.2"
ADAPTER_MODEL = "robworks-software/ccisd-teks-educator-mistral7b"

print(f"📥 Loading base model: {BASE_MODEL}...")
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)

# Check if GPU is available
device = "cuda" if torch.cuda.is_available() else "cpu"
print(f"   Using device: {device}")

# Load base model (use bfloat16 for CPU compatibility)
model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    torch_dtype=torch.bfloat16 if device == "cpu" else torch.float16,
    low_cpu_mem_usage=True
)

print(f"🔧 Loading LoRA adapter: {ADAPTER_MODEL}...")
model = PeftModel.from_pretrained(model, ADAPTER_MODEL)
model = model.to(device)
model.eval()  # Set to evaluation mode

print("✅ Model loaded successfully!")

def chat(message, history):
    """
    Generate response using Mistral chat format.

    Args:
        message: Current user message
        history: List of [user_msg, bot_msg] pairs

    Returns:
        Generated response string
    """

    # Simplified prompt - just the current message
    prompt = f"[INST] You are a Texas TEKS educational expert. Answer this question clearly and helpfully:\n\n{message} [/INST]"

    # Tokenize
    inputs = tokenizer(prompt, return_tensors="pt").to(device)

    # Generate response with better parameters
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_new_tokens=300,
            min_new_tokens=50,
            temperature=0.8,
            top_p=0.95,
            top_k=50,
            do_sample=True,
            repetition_penalty=1.1,
            pad_token_id=tokenizer.eos_token_id,
            eos_token_id=tokenizer.eos_token_id,
        )

    # Decode response
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)

    # Extract answer after [/INST]
    if "[/INST]" in response:
        response = response.split("[/INST]")[-1].strip()

    # Clean up any remaining artifacts
    if response.startswith(message):
        response = response[len(message):].strip()

    return response


# Create Gradio ChatInterface
demo = gr.ChatInterface(
    fn=chat,
    title="🎓 CCISD TEKS Educational Assistant",
    description="""
    **Powered by Mistral-7B fine-tuned on 4,224 enhanced CCISD TEKS examples**

    Ask questions about:
    - 📚 TEKS standard definitions and explanations
    - 👨‍🏫 Teaching strategies and lesson planning
    - 🌎 Real-world applications
    - ✅ Assessment and quiz generation
    - 🎯 Learning objectives and prerequisites
    - 💡 Common student misconceptions

    *Trained on Texas TEKS standards with real-world educational data.*
    """,
    examples=[
        "What is TEKS standard MATH.A1.3.B?",
        "How should I teach the scientific method to 7th grade students?",
        "What are real-world applications of linear equations?",
        "Create a quiz question to assess understanding of photosynthesis",
        "What are common misconceptions students have about fractions?",
        "Explain ELAR.9.1.A and suggest a lesson activity",
    ],
    cache_examples=False,  # Don't pre-compute example responses (too slow on CPU)
    theme="soft",
    chatbot=gr.Chatbot(height=500),
    textbox=gr.Textbox(placeholder="Ask me about Texas TEKS standards...", container=False, scale=7),
    retry_btn="🔄 Retry",
    undo_btn="↩️ Undo",
    clear_btn="🗑️ Clear",
)

# Launch the app
if __name__ == "__main__":
    demo.launch()