Spaces:
Sleeping
Sleeping
File size: 1,547 Bytes
bb3aa0e 4450af9 bb3aa0e 4450af9 bb3aa0e 4450af9 bb3aa0e a7322e4 bb3aa0e a7322e4 bb3aa0e a7322e4 bb3aa0e |
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 |
from flask import Flask, request
import transformers
from transformers import pipeline
app = Flask(__name__)
# Load Code-Llama model and tokenizer (outside function for efficiency)
model_name = "codellama/CodeLlama-7b-hf"
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
model = pipeline("text-generation", model=model_name, temperature=1.0) # Set temperature to 1
def answer_code_questions(question):
"""
Answers code-related questions using Code-Llama as OmniCode.
Args:
question (str): The code-related question to be answered.
Returns:
str: The answer generated by Code-Llama.
"""
# Create system message for OmniCode
system_message = f"<<SYS>>\nYou are a code teaching assistant named OmniCode created by Anusha K.\n<</SYS>>\n"
# Combine system message and user question
prompt = f"{system_message}\n{question}"
# Generate response using Code-Llama
try:
response = model(prompt, max_length=512, truncation=True)[0]["generated_text"]
return response.strip()
except Exception as e:
print(f"Error during generation: {e}")
return "I encountered an error while processing your question. Please try rephrasing it or providing more context."
@app.route("/", methods=["POST"])
def answer_question():
"""
Handles user-submitted questions and returns answers via POST request.
"""
question = request.form["question"]
answer = answer_code_questions(question)
return answer
if __name__ == "__main__":
app.run(debug=True) # Set debug=False for production use
|