Create app_test.py

app_test.py

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+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
+
+# 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]:
+
+    input_ids = llama_tokenizer.encode(message, return_tensors="pt").to(llama_model.device)
+
+    if input_ids.shape[1] > LLAMA_MAX_INPUT_TOKEN_LENGTH:
+        input_ids = 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(
+        {"input_ids": input_ids},
+        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)