Spaces:
Sleeping
Sleeping
File size: 2,404 Bytes
75b206c 01247e0 dea7d9a 01247e0 5baf039 2f006a2 01247e0 5baf039 2f006a2 01247e0 ca66e20 2f006a2 dea7d9a ca66e20 2f006a2 ca66e20 dea7d9a 2f006a2 dea7d9a 01247e0 5d1b435 2f006a2 01247e0 a8f1aa1 6c5f774 01247e0 5baf039 dea7d9a 2f006a2 dea7d9a 01247e0 2f006a2 dea7d9a 01247e0 5deaa96 dea7d9a 2f006a2 a8f1aa1 2f006a2 a8f1aa1 01247e0 a8f1aa1 01247e0 6c5f774 01247e0 6c5f774 01247e0 5baf039 5deaa96 2f006a2 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import streamlit as st
# Load base model and tokenizer
base_model = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
tokenizer = AutoTokenizer.from_pretrained(base_model)
# Load model in CPU mode
model = AutoModelForCausalLM.from_pretrained(
base_model,
torch_dtype=torch.float32,
device_map="cpu"
)
# Load the LoRA adapter
model = PeftModel.from_pretrained(model, "lora_adapter", device_map="cpu")
model.eval()
# Format prompt with example
def format_prompt(instruction):
return f"""### SYSTEM:
You are a helpful and expert Python programming tutor.
You only answer questions related to Python programming.
If the question is unrelated to Python, say:
"Sorry, I can only answer Python-related questions."
### USER:
What is the difference between a list and a tuple in Python?
### ASSISTANT:
In Python, both lists and tuples are used to store collections of items, but they have key differences. Lists are mutable (can be changed), whereas tuples are immutable (cannot be changed). Lists use square brackets [], and tuples use parentheses (). Tuples are generally faster and use less memory.
### USER:
{instruction}
### ASSISTANT:
"""
# Chat function
def chat(instruction):
prompt = format_prompt(instruction)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(
**inputs,
max_new_tokens=256,
do_sample=False,
temperature=0.0,
top_p=1.0,
repetition_penalty=1.1
)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("🧠 Raw Model Output:", repr(response)) # Debug log
# Extract response cleanly
if "### ASSISTANT:" in response:
return response.split("### ASSISTANT:")[-1].strip()
else:
return response.strip() # fallback
# Streamlit UI
st.set_page_config(page_title="🐍 Python Tutor Chatbot")
st.title("🐍 Python Tutor Chatbot")
st.write("Ask me Python programming questions!")
user_input = st.text_area("Your question:")
if st.button("Get Answer") and user_input.strip():
with st.spinner("Thinking..."):
response = chat(user_input)
st.markdown("**Answer:**")
st.write(response)
# Optional debug output
# st.write("**Raw model output:**")
# st.write(repr(response))
|