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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel
print("🚀 Loading AI/ML Tutor with Vicuna...")
# Configure 4-bit quantization to reduce memory usage
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.float16
)
base_model = "TheBloke/vicuna-7B-1.1-HF"
adapter_path = "shahzaib41202/ai-ml-tutor-v1"
try:
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(adapter_path)
tokenizer.pad_token = tokenizer.eos_token
# Load base model with 4-bit quantization
model = AutoModelForCausalLM.from_pretrained(
base_model,
quantization_config=bnb_config,
device_map="auto",
trust_remote_code=True
)
# Load your fine-tuned adapter
model = PeftModel.from_pretrained(model, adapter_path)
print("✅ Vicuna AI Tutor Loaded with 4-bit quantization!")
except Exception as e:
print(f"❌ Failed to load model: {e}")
model = None
tokenizer = None
def ask_ai_tutor(question):
if model is None or tokenizer is None:
return "AI Tutor is currently unavailable. Please try again later."
try:
prompt = f"Question: {question}\nAnswer (from AI ML Tutor created by Shahzaib Shaikh):"
# Tokenize
inputs = tokenizer(
prompt,
return_tensors="pt",
truncation=True,
max_length=256 # Reduced for memory
)
# Generate with minimal memory usage
with torch.no_grad():
outputs = model.generate(
**inputs,
max_new_tokens=150, # Shorter responses to save memory
temperature=0.7,
do_sample=True,
top_p=0.9,
repetition_penalty=1.1
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
if "Answer (from AI ML Tutor created by Shahzaib Shaikh):" in response:
answer = response.split("Answer (from AI ML Tutor created by Shahzaib Shaikh):")[-1].strip()
else:
answer = response
return answer
except Exception as e:
return f"❌ Generation error: {str(e)}"
# Create interface
with gr.Blocks(theme=gr.themes.Soft()) as demo:
gr.Markdown("# 🤖 AI/ML Tutor by Shahzaib Shaikh")
gr.Markdown("### Powered by Vicuna-7B (4-bit quantized)")
with gr.Row():
with gr.Column():
question = gr.Textbox(
label="Your Question",
placeholder="Ask about Machine Learning, NLP, Random Forest, Decision Trees...",
lines=3
)
ask_btn = gr.Button("🚀 Ask AI Tutor", variant="primary")
answer = gr.Textbox(
label="AI Tutor Response",
lines=6,
interactive=False,
show_copy_button=True
)
clear_btn = gr.Button("🗑️ Clear")
with gr.Column():
gr.Markdown("### 💡 Quick Examples")
gr.Examples(
examples=[
"What is Machine Learning?",
"What is NLP?",
"What is Random Forest?",
"What is Decision Tree?"
],
inputs=question,
label="Click any example"
)
gr.Markdown("### ℹ️ Note")
gr.Markdown("Using Vicuna-7B with 4-bit quantization for memory efficiency")
def get_answer(q):
if not q.strip():
return "Please enter a question."
return ask_ai_tutor(q)
def clear_all():
return "", ""
ask_btn.click(get_answer, question, answer)
question.submit(get_answer, question, answer)
clear_btn.click(clear_all, outputs=[question, answer])
if __name__ == "__main__":
demo.launch() |