app.py

import gradio as gr
import random
import numpy as np
import pandas as pd
from datasets import load_dataset
from sentence_transformers import CrossEncoder
from sklearn.metrics import average_precision_score
import matplotlib.pyplot as plt
import torch
import spaces
import os
from huggingface_hub import HfApi

# Load Hugging Face token from the environment variable
HF_TOKEN = os.getenv("HF_TOKEN")
if HF_TOKEN is None:
    raise ValueError("HF_TOKEN environment variable is not set. Please set it before running the script.")


hf_api = HfApi(
    token= HF_TOKEN, # Token is not persisted on the machine.
)

# Check for GPU support and configure appropriately
device = "cuda" if torch.cuda.is_available() else "cpu"
zero = torch.Tensor([0]).to(device)
print(f"Device being used: {zero.device}")

# Define evaluation metrics
def mean_reciprocal_rank(relevance_labels, scores):
    sorted_indices = np.argsort(scores)[::-1]
    for rank, idx in enumerate(sorted_indices, start=1):
        if relevance_labels[idx] == 1:
            return 1 / rank
    return 0

def mean_average_precision(relevance_labels, scores):
    return average_precision_score(relevance_labels, scores)

def ndcg_at_k(relevance_labels, scores, k=10):
    sorted_indices = np.argsort(scores)[::-1]
    relevance_sorted = np.take(relevance_labels, sorted_indices[:k])
    dcg = sum(rel / np.log2(rank + 2) for rank, rel in enumerate(relevance_sorted))
    idcg = sum(1 / np.log2(rank + 2) for rank in range(min(k, sum(relevance_labels))))
    return dcg / idcg if idcg > 0 else 0

# Load datasets
datasets = {
    "Relevance_Labels_Dataset": load_dataset("Omartificial-Intelligence-Space/re-ar-test-triplet" , token =HF_TOKEN )["train"].select(range(100)),
    "Positive_Negatives_Dataset": load_dataset("Omartificial-Intelligence-Space/re-ar-query-candidate" , token =HF_TOKEN )["train"].select(range(100))
}

@spaces.GPU(duration=120)
def evaluate_model_with_insights(model_name):
    model = CrossEncoder(model_name, device=device)
    results = []
    sample_outputs = []

    for dataset_name, dataset in datasets.items():
        all_mrr, all_map, all_ndcg = [], [], []
        dataset_samples = []

        if 'candidate_document' in dataset.column_names:
            grouped_data = dataset.to_pandas().groupby("query")
            for query, group in grouped_data:
                candidate_texts = group['candidate_document'].tolist()
                relevance_labels = group['relevance_label'].tolist()
                pairs = [(query, doc) for doc in candidate_texts]
                scores = model.predict(pairs)

                # Collecting top-5 results for display
                sorted_indices = np.argsort(scores)[::-1]
                top_docs = [(candidate_texts[i], scores[i], relevance_labels[i]) for i in sorted_indices[:5]]
                dataset_samples.append({
                    "Query": query,
                    "Top 5 Candidates": top_docs
                })

                # Metrics
                all_mrr.append(mean_reciprocal_rank(relevance_labels, scores))
                all_map.append(mean_average_precision(relevance_labels, scores))
                all_ndcg.append(ndcg_at_k(relevance_labels, scores, k=10))
        else:
            for entry in dataset:
                query = entry['query']
                candidate_texts = [entry['positive'], entry['negative1'], entry['negative2'], entry['negative3'], entry['negative4']]
                relevance_labels = [1, 0, 0, 0, 0]
                pairs = [(query, doc) for doc in candidate_texts]
                scores = model.predict(pairs)

                # Collecting top-5 results for display
                sorted_indices = np.argsort(scores)[::-1]
                top_docs = [(candidate_texts[i], scores[i], relevance_labels[i]) for i in sorted_indices[:5]]
                dataset_samples.append({
                    "Query": query,
                    "Top 5 Candidates": top_docs
                })

                # Metrics
                all_mrr.append(mean_reciprocal_rank(relevance_labels, scores))
                all_map.append(mean_average_precision(relevance_labels, scores))
                all_ndcg.append(ndcg_at_k(relevance_labels, scores, k=10))
        
        # Metrics for this dataset
        results.append({
            "Dataset": dataset_name,
            "MRR": np.mean(all_mrr),
            "MAP": np.mean(all_map),
            "nDCG@10": np.mean(all_ndcg)
        })

        # Collect sample outputs for inspection
        sample_outputs.extend(dataset_samples)

    results_df = pd.DataFrame(results)

    # Plot results as a bar chart
    fig, ax = plt.subplots(figsize=(8, 6))
    results_df.plot(kind='bar', x='Dataset', y=['MRR', 'MAP', 'nDCG@10'], ax=ax)
    ax.set_title(f"Evaluation Results for {model_name}")
    ax.set_ylabel("Score")
    plt.xticks(rotation=0)

    return results_df, fig, sample_outputs

# Gradio app interface
def gradio_app_with_insights(model_name):
    results_df, chart, samples = evaluate_model_with_insights(model_name)
    sample_display = []
    for sample in samples:
        sample_display.append(f"Query: {sample['Query']}")
        for doc, score, label in sample["Top 5 Candidates"]:
            sample_display.append(f"    Doc: {doc[:50]}... | Score: {score:.2f} | Relevance: {label}")
        sample_display.append("\n")
    return results_df, chart, "\n".join(sample_display)

interface = gr.Interface(
    fn=gradio_app_with_insights,
    inputs=gr.Textbox(label="Enter Model Name", placeholder="e.g., NAMAA-Space/GATE-Reranker-V1"),
    outputs=[
        gr.Dataframe(label="Evaluation Results"),
        gr.Plot(label="Evaluation Metrics Chart"),
        gr.Textbox(label="Sample Reranking Insights", lines=15)
    ],
    title="Arabic Reranking Model Evaluation and Insights",
    description=(
        "This app evaluates Arabic reranking models on two datasets:\n"
        "1. **Relevance Labels Dataset**\n"
        "2. **Positive-Negatives Dataset**\n\n"
        "### Metrics Used:\n"
        "- **MRR (Mean Reciprocal Rank)**: Measures how quickly the first relevant document appears.\n"
        "- **MAP (Mean Average Precision)**: Reflects ranking quality across all relevant documents.\n"
        "- **nDCG@10 (Normalized Discounted Cumulative Gain)**: Focuses on the ranking of relevant documents in the top-10.\n\n"
        "Input a model name to evaluate its performance, view metrics, and examine sample reranking results."
    )
)

interface.launch(debug=True)