"""
Gradio app for PR Reviewer Assignment Model.

This application provides an interactive interface for predicting PR reviewers
based on PR title and modified files using a fine-tuned DeBERTa model.

For private models, set the HF_TOKEN environment variable:
    export HF_TOKEN=your_huggingface_token
"""

import gradio as gr
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
import json
import os


# Model configuration
MODEL_NAME = (
    "yazoniak/pr-assignee-reviewer-deberta"  # Update with your actual model name
)
MAX_LENGTH = 8192
DEFAULT_THRESHOLD = 0.5

# Authentication token for private models
HF_TOKEN = os.environ.get("HF_TOKEN", None)


def load_model():
    """
    Load the model and tokenizer.

    For private models, requires HF_TOKEN environment variable to be set.

    Returns:
        tuple: (model, tokenizer, id2label)
    """
    if HF_TOKEN:
        print(f"Using authentication token for private model: {MODEL_NAME}")
        model = AutoModelForSequenceClassification.from_pretrained(
            MODEL_NAME, token=HF_TOKEN
        )
        tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, token=HF_TOKEN)
    else:
        print(f"No token found, attempting to load public model: {MODEL_NAME}")
        print("If this is a private model, set HF_TOKEN environment variable")
        model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
        tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)

    model.eval()

    # Get label mappings from model config
    id2label = model.config.id2label
    if id2label and isinstance(list(id2label.keys())[0], str):
        id2label = {int(k): v for k, v in id2label.items()}

    return model, tokenizer, id2label


# Load model at startup
print("Loading model...")
model, tokenizer, id2label = load_model()
print(f"Model loaded successfully with {len(id2label)} reviewers")


def predict_reviewers(
    pr_title: str,
    files_input: str,
    threshold: float = DEFAULT_THRESHOLD,
    custom_mapping: str = "",
) -> tuple[str, str]:
    """
    Predict reviewers for a PR based on title and modified files.

    Args:
        pr_title: The PR title/description
        files_input: Comma or semicolon separated list of modified files
        threshold: Prediction threshold (0-1)
        custom_mapping: Optional JSON mapping of label IDs to names

    Returns:
        tuple: (formatted_predictions, all_scores_json)
    """
    # Validate inputs
    if not pr_title or not pr_title.strip():
        return "⚠️ Please enter a PR title", ""

    if not files_input or not files_input.strip():
        return "⚠️ Please enter at least one file", ""

    # Parse files list
    files_list = []
    for separator in [",", ";"]:
        if separator in files_input:
            files_list = [f.strip() for f in files_input.split(separator) if f.strip()]
            break
    if not files_list:
        files_list = [files_input.strip()]

    # Validate threshold
    if threshold < 0 or threshold > 1:
        return "⚠️ Threshold must be between 0 and 1", ""

    # Parse custom mapping if provided
    label_mapping = id2label  # Default to model's labels
    if custom_mapping and custom_mapping.strip():
        try:
            parsed_mapping = json.loads(custom_mapping)
            # Convert string keys to integers
            label_mapping = {int(k): v for k, v in parsed_mapping.items()}
        except json.JSONDecodeError:
            return "⚠️ Invalid JSON format for custom mapping", ""
        except (ValueError, TypeError):
            return "⚠️ Custom mapping must have numeric keys", ""

    # Format input for the model
    files_text = f"files: {', '.join(files_list)}"

    # Tokenize
    inputs = tokenizer(
        [pr_title],
        text_pair=[files_text],
        truncation=True,
        max_length=MAX_LENGTH,
        padding=True,
        return_tensors="pt",
    )

    # Make prediction
    with torch.no_grad():
        outputs = model(**inputs)
        logits = outputs.logits
        probabilities = torch.sigmoid(logits).numpy()[0]

    # Get predictions above threshold
    predicted_reviewers = []
    all_scores = {}

    for idx, prob in enumerate(probabilities):
        reviewer_name = label_mapping.get(idx, f"label_{idx}")
        all_scores[reviewer_name] = float(prob)

        if prob > threshold:
            predicted_reviewers.append(
                {"reviewer": reviewer_name, "confidence": float(prob)}
            )

    # Sort by confidence
    predicted_reviewers.sort(key=lambda x: x["confidence"], reverse=True)

    # Format output
    result_text = "## Prediction Results\n\n"
    result_text += f"**PR Title:** {pr_title}\n\n"
    result_text += f"**Files ({len(files_list)}):** {', '.join(files_list[:5])}"
    if len(files_list) > 5:
        result_text += f" ... and {len(files_list) - 5} more"
    result_text += f"\n\n**Threshold:** {threshold:.2f}\n\n"

    if predicted_reviewers:
        result_text += f"### Predicted Reviewers ({len(predicted_reviewers)})\n\n"
        for i, pred in enumerate(predicted_reviewers, 1):
            confidence_bar = "🟩" * int(pred["confidence"] * 10)
            result_text += f"{i}. **{pred['reviewer']}** - {pred['confidence']:.3f} {confidence_bar}\n"
    else:
        result_text += "### No Reviewers Predicted\n\n"
        result_text += "All confidence scores are below the threshold.\n"

    # Show top 5 scores regardless of threshold
    top_scores = sorted(all_scores.items(), key=lambda x: x[1], reverse=True)[:5]
    result_text += "\n### Top 5 Confidence Scores\n\n"
    for reviewer, score in top_scores:
        confidence_bar = "🟦" * int(score * 10)
        result_text += f"- **{reviewer}**: {score:.3f} {confidence_bar}\n"

    # Create JSON output for all scores
    all_scores_json = json.dumps(
        {
            "predicted_reviewers": predicted_reviewers,
            "all_scores": all_scores,
            "threshold": threshold,
            "num_files": len(files_list),
        },
        indent=2,
    )

    return result_text, all_scores_json


# Example inputs
examples = [
    [
        "Fix authentication bug in user service",
        "auth.py, user.py, test_auth.py",
        0.5,
        "",
    ],
    [
        "Add new payment gateway integration",
        "gateway.py; payment_routes.py; config.py",
        0.5,
        "",
    ],
]


# Create Gradio interface
with gr.Blocks(title="PR Reviewer Assignment", theme=gr.themes.Soft()) as demo:
    gr.Markdown("""
    # PR Reviewer Assignment Model
    
    This model predicts the best team members to review a Pull Request based on:
    - **PR Title/Description**: What the PR is about
    - **Modified Files**: Which files are being changed
    
    The model uses a fine-tuned **DeBERTa-large** model trained on historical PR patterns.
    """)

    with gr.Row():
        with gr.Column(scale=2):
            pr_title_input = gr.Textbox(
                label="PR Title/Description",
                placeholder="e.g., Fix authentication bug in user service",
                lines=2,
            )

            files_input = gr.Textbox(
                label="Modified Files (comma or semicolon separated)",
                placeholder="e.g., auth.py, user.py, test_auth.py",
                lines=3,
            )

            threshold_input = gr.Slider(
                minimum=0.0,
                maximum=1.0,
                value=DEFAULT_THRESHOLD,
                step=0.05,
                label="Prediction Threshold",
                info="Only show predictions above this confidence score",
            )

            with gr.Accordion("Custom Label Mapping (Optional)", open=False):
                gr.Markdown(
                    """
                    If your deployed model has generic labels (e.g., `label_0`, `label_1`),
                    you can paste your own ID to name mapping here in JSON format.
                    
                    **Example format:**
                    ```json
                    {
                      "0": "John Doe",
                      "1": "Jane Smith",
                      "2": "Bob Johnson"
                    }
                    ```
                    """
                )
                custom_mapping_input = gr.Code(
                    label="Custom ID to Label Mapping (JSON)",
                    language="json",
                    lines=10,
                    value="",
                )

            predict_btn = gr.Button("Predict Reviewers", variant="primary", size="lg")

        with gr.Column(scale=3):
            prediction_output = gr.Markdown(label="Predictions")

    with gr.Accordion("📋 Detailed JSON Output", open=False):
        json_output = gr.JSON(label="Full Prediction Details")

    # Connect the button
    predict_btn.click(
        fn=predict_reviewers,
        inputs=[pr_title_input, files_input, threshold_input, custom_mapping_input],
        outputs=[prediction_output, json_output],
    )

    # Examples section
    gr.Markdown("### Example Inputs")
    gr.Examples(
        examples=examples,
        inputs=[pr_title_input, files_input, threshold_input, custom_mapping_input],
        outputs=[prediction_output, json_output],
        fn=predict_reviewers,
        cache_examples=False,
    )

    gr.Markdown("""
    ---
    ### Model Performance
    
    | Metric | Score |
    |--------|-------|
    | F1 Macro | 0.76 |
    | F1 Micro | 0.83 |
    | F1 Weighted | 0.82 |
    | Subset Accuracy | 0.83 |
    
    ### How to Use
    
    1. **Enter PR Title**: Describe what the PR is about
    2. **List Modified Files**: Enter file names separated by commas or semicolons
    3. **Adjust Threshold** (optional): Lower threshold = more suggestions, Higher threshold = only high-confidence suggestions
    4. **Click Predict**: Get reviewer recommendations with confidence scores
    
    ### Limitations
    
    - Model is trained on specific team patterns and may not generalize to other teams
    - Uses only file names and PR titles, not actual code changes
    - New team members may not be predicted accurately without historical data
    """)


# Launch the app
if __name__ == "__main__":
    demo.launch()