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import streamlit as st
from transformers import pipeline
import csv
import re
import torch
import warnings
warnings.filterwarnings("ignore")
# Define a prompt template for Magicoder with placeholders for instruction and response.
MAGICODER_PROMPT = """You are an exceptionally intelligent coding assistant that consistently delivers accurate and reliable responses to user instructions.
@@ Instruction
{instruction}
@@ Response
"""
@st.cache(allow_output_mutation=True)
def load_model():
return pipeline(
model="ise-uiuc/Magicoder-S-DS-6.7B",
task="text-generation"
)
# Function to generate response
def generate_response(instruction):
prompt = MAGICODER_PROMPT.format(instruction=instruction)
result = model(prompt, max_length=2048, num_return_sequences=1, temperature=0.0)
response = result[0]["generated_text"]
response_start_index = response.find("@@ Response") + len("@@ Response")
response = response[response_start_index:].strip()
return response
# Function to append data to a CSV file
def save_to_csv(data, filename):
with open(filename, 'a', newline='') as csvfile:
writer = csv.writer(csvfile)
writer.writerow(data)
# Streamlit app
def main():
global model
st.title("Magicoder Assistant")
if 'model' not in globals():
model = load_model()
instruction = st.text_area("Enter your instruction here:")
if st.button("Generate Response"):
generated_response = generate_response(instruction)
st.text("Generated response:")
st.text(generated_response)
correct_output = st.radio("Is the generated output correct?", ("Yes", "No"))
if correct_output.lower() == 'yes':
feedback = st.text_input("Do you want to provide any feedback?")
save_to_csv(["Correct", feedback], 'output_ratings.csv')
else:
correct_code = st.text_area("Please enter the correct code:")
feedback = st.text_input("Any other feedback you want to provide:")
save_to_csv(["Incorrect", feedback, correct_code], 'output_ratings.csv')
if __name__ == "__main__":
main()
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