app.py

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)  # Or an even larger value if needed


# 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)

# --- Quality Prediction Model Setup ---
model_paths = [
    'karths/binary_classification_train_test',
    "karths/binary_classification_train_process",
    "karths/binary_classification_train_infrastructure",
    "karths/binary_classification_train_documentation",
    "karths/binary_classification_train_design",
    "karths/binary_classification_train_defect",
    "karths/binary_classification_train_code",
    "karths/binary_classification_train_build",
    "karths/binary_classification_train_automation",
    "karths/binary_classification_train_people",
    "karths/binary_classification_train_architecture",
]

quality_mapping = {
    'binary_classification_train_test': 'Test',
    'binary_classification_train_process': 'Process',
    'binary_classification_train_infrastructure': 'Infrastructure',
    'binary_classification_train_documentation': 'Documentation',
    'binary_classification_train_design': 'Design',
    'binary_classification_train_defect': 'Defect',
    'binary_classification_train_code': 'Code',
    'binary_classification_train_build': 'Build',
    'binary_classification_train_automation': 'Automation',
    'binary_classification_train_people': 'People',
    'binary_classification_train_architecture': 'Architecture'
}

# Pre-load models and tokenizer for quality prediction
tokenizer = AutoTokenizer.from_pretrained("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")

@spaces.GPU
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])
    return avg_prob

# --- Llama 3.2 3B Model Setup ---
LLAMA_MAX_MAX_NEW_TOKENS = 2048
LLAMA_DEFAULT_MAX_NEW_TOKENS = 1024
LLAMA_MAX_INPUT_TOKEN_LENGTH = int(os.getenv("MAX_INPUT_TOKEN_LENGTH", "4096"))
llama_device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")  # Explicitly define device
llama_model_id = "meta-llama/Llama-3.2-3B-Instruct"
llama_tokenizer = AutoTokenizer.from_pretrained(llama_model_id)
llama_model = AutoModelForCausalLM.from_pretrained(
    llama_model_id,
    device_map="auto",  # Automatically distribute model across devices
    torch_dtype=torch.bfloat16,
)
llama_model.eval()


@spaces.GPU(duration=90)
def llama_generate(
    message: str,
    max_new_tokens: int = LLAMA_DEFAULT_MAX_NEW_TOKENS,
    temperature: float = 0.6,
    top_p: float = 0.9,
    top_k: int = 50,
    repetition_penalty: float = 1.2,
) -> Iterator[str]:

    inputs = llama_tokenizer(message, return_tensors="pt", padding=True, truncation=True, max_length=LLAMA_MAX_INPUT_TOKEN_LENGTH).to(llama_model.device)
    #The line above was changed to add attention mask

    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.")

    streamer = TextIteratorStreamer(llama_tokenizer, timeout=60.0, skip_prompt=True, skip_special_tokens=True)
    generate_kwargs = dict(
        inputs,  # Pass the entire inputs dictionary
        streamer=streamer,
        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,
    )
    t = Thread(target=llama_model.generate, kwargs=generate_kwargs)
    t.start()
    outputs = []
    for text in streamer:
        outputs.append(text)
        yield "".join(outputs)



def generate_explanation(issue_text, top_qualities):
    """Generates an explanation using Llama 3.2 3B."""
    if not top_qualities:
        return "No explanation available as no quality tags were predicted."

    prompt = f"""
    Given the following issue description:
    ---
    {issue_text}
    ---
    Explain why this issue might be classified under the following quality categories: {', '.join([q[0] for q in top_qualities])}. 
    Provide a concise explanation for each category, relating it back to the issue description.
    """
    explanation = ""
    try:
        for chunk in llama_generate(prompt):
            explanation += chunk  # Accumulate generated text
    except Exception as e:
        logging.error(f"Error during Llama generation: {e}")
        return "An error occurred while generating the explanation."

    return explanation


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:
            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>", "", ""

    top_qualities = sorted(results, key=lambda x: x[1], reverse=True)[:3]
    output_html = render_html_output(top_qualities)

    # Generate explanation using the top qualities and the original input text
    explanation = generate_explanation(text, top_qualities)

    return output_html, "", explanation  # Return explanation as the third output

def render_html_output(top_qualities):
    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);
        }
        .probability {
            display: block;
            margin-top: 10px;
            font-size: 16px;
            color: #007bff;
        }
    </style>
    """
    html_content = ""
    ranking_labels = ['Top 1 Prediction', 'Top 2 Prediction', 'Top 3 Prediction']
    top_n = min(len(top_qualities), len(ranking_labels))
    for i in range(top_n):
        quality, prob = top_qualities[i]
        html_content += f"""
        <div class="quality-container">
            <span class="ranking">{ranking_labels[i]}</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."]
]

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.Textbox(label="Explanation", lines=5)  # Added Textbox for explanation
    ],
    title="QualityTagger",
    description="This tool classifies text into different quality domains such as Security, Usability, etc., and provides explanations.",
    examples=example_texts
)
interface.launch(share=True)