import gradio as gr
import os
import torch
import numpy as np
import random
from huggingface_hub import login, HfFolder
from transformers import AutoTokenizer, AutoModelForSequenceClassification, AutoModelForCausalLM, TextIteratorStreamer
from scipy.special import softmax
import logging
import spaces
from threading import Thread
from collections.abc import Iterator
import csv
# Increase CSV field size limit
csv.field_size_limit(1000000)
# Setup logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(message)s')
# Set a seed for reproducibility
seed = 42
np.random.seed(seed)
random.seed(seed)
torch.manual_seed(seed)
if torch.cuda.is_available():
torch.cuda.manual_seed_all(seed)
# Login to Hugging Face
token = os.getenv("hf_token")
HfFolder.save_token(token)
login(token)
model_paths = [
'karths/binary_classification_train_port',
'karths/binary_classification_train_perf',
"karths/binary_classification_train_main",
"karths/binary_classification_train_secu",
"karths/binary_classification_train_reli",
"karths/binary_classification_train_usab",
"karths/binary_classification_train_comp"
]
quality_mapping = {
'binary_classification_train_port': 'Portability',
'binary_classification_train_main': 'Maintainability',
'binary_classification_train_secu': 'Security',
'binary_classification_train_reli': 'Reliability',
'binary_classification_train_usab': 'Usability',
'binary_classification_train_perf': 'Performance',
'binary_classification_train_comp': 'Compatibility'
}
# Pre-load models and tokenizer for quality prediction
tokenizer = AutoTokenizer.from_pretrained("distilbert/distilroberta-base")
models = {path: AutoModelForSequenceClassification.from_pretrained(path) for path in model_paths}
def get_quality_name(model_name):
return quality_mapping.get(model_name.split('/')[-1], "Unknown Quality")
def model_prediction(model, text, device):
model.to(device)
model.eval()
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
inputs = {k: v.to(device) for k, v in inputs.items()}
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = softmax(logits.cpu().numpy(), axis=1)
avg_prob = np.mean(probs[:, 1])
model.to("cpu")
return avg_prob
# --- Llama 3.2 3B Model Setup ---
LLAMA_MAX_MAX_NEW_TOKENS = 512
LLAMA_DEFAULT_MAX_NEW_TOKENS = 250
LLAMA_MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "1024"))
llama_device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
llama_model_id = "meta-llama/Llama-3.2-1B-Instruct"
llama_tokenizer = AutoTokenizer.from_pretrained(llama_model_id)
llama_model = AutoModelForCausalLM.from_pretrained(
llama_model_id,
device_map="auto",
torch_dtype=torch.bfloat16,
)
llama_model.eval()
if llama_tokenizer.pad_token is None:
llama_tokenizer.pad_token = llama_tokenizer.eos_token
def llama_generate(
message: str,
max_new_tokens: int = LLAMA_DEFAULT_MAX_NEW_TOKENS,
temperature: float = 0.2,
top_p: float = 0.9,
top_k: int = 50,
repetition_penalty: float = 1.2,
) -> str:
inputs = llama_tokenizer(message, return_tensors="pt", padding=True, truncation=True, max_length=LLAMA_MAX_INPUT_TOKEN_LENGTH).to(llama_model.device)
if inputs.input_ids.shape[1] > LLAMA_MAX_INPUT_TOKEN_LENGTH:
inputs.input_ids = inputs.input_ids[:, -LLAMA_MAX_INPUT_TOKEN_LENGTH:]
gr.Warning(f"Trimmed input from conversation as it was longer than {LLAMA_MAX_INPUT_TOKEN_LENGTH} tokens.")
with torch.no_grad():
generate_ids = llama_model.generate(
**inputs,
max_new_tokens=max_new_tokens,
do_sample=True,
top_p=top_p,
top_k=top_k,
temperature=temperature,
num_beams=1,
repetition_penalty=repetition_penalty,
pad_token_id=llama_tokenizer.pad_token_id,
eos_token_id=llama_tokenizer.eos_token_id,
)
output_text = llama_tokenizer.decode(generate_ids[0], skip_special_tokens=True)
torch.cuda.empty_cache()
return output_text
def generate_explanation(issue_text, top_quality):
"""Generates an explanation for the *single* top quality above threshold."""
if not top_quality:
return "<div style='color: red;'>No explanation available as no quality tags met the threshold.</div>"
quality_name = top_quality[0][0] # Get the name of the top quality
prompt = f"""
Given the following issue description:
---
{issue_text}
Explain why this issue might be classified as a **{quality_name}** issue. Provide a concise explanation, relating it back to the issue description. Keep the explanation short and concise. Do not repeat the prompt or include any preamble in your response - just provide the explanation directly.
<br>
---
"""
try:
explanation = llama_generate(prompt)
# Extract only the model's explanation, removing any prompt repetition
# This typically removes any preamble like "Here's why this is a [quality] issue:"
cleaned_explanation = explanation.split("---")[-1].strip()
if cleaned_explanation.lower().startswith(quality_name.lower()):
cleaned_explanation = cleaned_explanation[len(quality_name):].strip()
if cleaned_explanation.startswith(":"):
cleaned_explanation = cleaned_explanation[1:].strip()
# Format for better readability
formatted_explanation = f"<div class='explanation-box'><p><b>Why this is a {quality_name} issue:</b></p><p>{cleaned_explanation}</p></div>"
return formatted_explanation
except Exception as e:
logging.error(f"Error during Llama generation: {e}")
return "<div style='color: red;'>An error occurred while generating the explanation.</div>"
@spaces.GPU(duration=90)
def main_interface(text):
if not text.strip():
return "<div style='color: red;'>No text provided. Please enter a valid issue description.</div>", "", ""
if len(text) < 30:
return "<div style='color: red;'>Text is less than 30 characters.</div>", "", ""
device = "cuda" if torch.cuda.is_available() else "cpu"
results = []
for model_path, model in models.items():
quality_name = get_quality_name(model_path)
avg_prob = model_prediction(model, text, device)
if avg_prob >= 0.95: # Keep *all* results above the threshold
results.append((quality_name, avg_prob))
logging.info(f"Model: {model_path}, Quality: {quality_name}, Average Probability: {avg_prob:.3f}")
if not results:
return "<div style='color: red;'>No recommendation. Prediction probability is below the threshold.</div>", "", ""
# Sort and get the top result (if any meet the threshold)
top_result = sorted(results, key=lambda x: x[1], reverse=True)
if top_result:
top_quality = top_result[:1] # Select only the top result
output_html = render_html_output(top_quality)
explanation = generate_explanation(text, top_quality)
else: # Handle case no predictions >= 0.95
output_html = "<div style='color: red;'>No quality tag met the prediction probability threshold (>= 0.95).</div>"
explanation = ""
return output_html, "", explanation
def render_html_output(top_qualities):
#Simplified to show only the top prediction
styles = """
<style>
.quality-container {
font-family: Arial, sans-serif;
text-align: center;
margin-top: 20px;
}
.quality-label, .ranking {
display: inline-block;
padding: 0.5em 1em;
font-size: 18px;
font-weight: bold;
color: white;
background-color: #007bff;
border-radius: 0.5rem;
margin-right: 10px;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.2);
}
</style>
"""
if not top_qualities: # Handle empty case
return styles + "<div class='quality-container'>No Top Prediction</div>"
quality, _ = top_qualities[0] #We know there is only one
html_content = f"""
<div class="quality-container">
<span class="ranking">Top Prediction</span>
<span class="quality-label">{quality}</span>
</div>
"""
return styles + html_content
example_texts = [
["The algorithm does not accurately distinguish between the positive and negative classes during edge cases.\n\nEnvironment: Production\nReproduction: Run the classifier on the test dataset with known edge cases."],
["The regression tests do not cover scenarios involving concurrent user sessions.\n\nEnvironment: Test automation suite\nReproduction: Update the test scripts to include tests for concurrent sessions."],
["There is frequent miscommunication between the development and QA teams regarding feature specifications.\n\nEnvironment: Inter-team meetings\nReproduction: Audit recent communication logs and meeting notes between the teams."],
["The service-oriented architecture does not effectively isolate failures, leading to cascading failures across services.\n\nEnvironment: Microservices architecture\nReproduction: Simulate a service failure and observe the impact on other services."]
]
# Improved CSS for better layout and appearance
css = """
.quality-container {
font-family: Arial, sans-serif;
text-align: center;
margin-top: 20px;
padding: 10px;
border: 1px solid #ddd;
border-radius: 8px;
background-color: #f9f9f9;
}
.quality-label, .ranking {
display: inline-block;
padding: 0.5em 1em;
font-size: 18px;
font-weight: bold;
color: white;
background-color: #007bff;
border-radius: 0.5rem;
margin-right: 10px;
box-shadow: 0 2px 4px rgba(0, 0, 0, 0.2);
}
.explanation-box {
border: 1px solid #ccc;
padding: 15px;
margin-top: 15px;
border-radius: 8px;
background-color: #fff;
box-shadow: 0 1px 3px rgba(0,0,0,0.1);
line-height: 1.5;
}
.explanation-box p {
margin: 8px 0;
}
.explanation-box b {
color: #007bff;
}
"""
interface = gr.Interface(
fn=main_interface,
inputs=gr.Textbox(lines=7, label="Issue Description", placeholder="Enter your issue text here"),
outputs=[
gr.HTML(label="Prediction Output"),
gr.Textbox(label="Predictions", visible=False),
gr.Markdown(label="Explanation")
],
title="QualityTagger",
description="This tool classifies text into different quality domains such as Security, Usability,Mantainability, Reliability etc., and provides explanations.",
examples=example_texts,
css=css,
cache_examples=False# Apply the CSS
)
interface.launch( )