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import gradio as gr
from transformers import pipeline
import torch
import csv
import re
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
"""
# Create a text generation pipeline using the Magicoder model, text-generation task, bfloat16 torch data type and auto device mapping.
generator = pipeline(
model="ise-uiuc/Magicoder-S-DS-6.7B",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
# Function to generate response
def generate_response(instruction):
prompt = MAGICODER_PROMPT.format(instruction=instruction)
result = generator(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)
# Function to process user feedback
def process_output(correct_output, feedback=None, correct_code=None):
if correct_output.lower() == 'yes':
save_to_csv(["Correct", feedback], 'output_ratings.csv')
else:
save_to_csv(["Incorrect", feedback, correct_code], 'output_ratings.csv')
# Gradio interface
input_text = gr.inputs.Textbox(lines=5, label="Enter your instruction here:")
output_text = gr.outputs.Textbox(label="Generated response:")
def generate_and_process(instruction, correct_output):
generated_response = generate_response(instruction)
if correct_output.lower() == "no":
feedback = gr.Interface(
lambda x: x,
gr.inputs.Textbox(lines=5, label="Please enter the correct code:"),
"text"
).launch()
correct_code = feedback
process_output(correct_output, feedback, correct_code)
else:
feedback = ""
process_output(correct_output, feedback)
return generated_response
title = "Magicoder Assistant"
description = "An intelligent coding assistant that generates responses based on user instructions."
examples = [["Implement a high-level API for a TODO list application. The API takes as input an operation request and updates the TODO list in place. If the request is invalid, raise an exception."]]
gr.Interface(
generate_and_process,
inputs=[input_text, "radio"],
outputs=output_text,
title=title,
description=description,
examples=examples
).launch()
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