use pyo3::prelude::*;
use pyo3::exceptions::PyValueError;
use serde::{Deserialize, Serialize};
use serde_json::{Value, json};

#[derive(Deserialize)]
struct MultimodalEvalInput {
    inputs: Value,      // {"text": [...], "images": [...], ...}
    outputs: Value,     // model responses
    modalities: Vec<String>,
    metrics: Option<Vec<String>>,
}

#[derive(Serialize)]
struct MultimodalEvalResult {
    cross_modal_alignment: f64,
    visual_grounding: f64,
    multimodal_hallucination: f64,
}

#[derive(Deserialize)]
struct GenerationEvalInput {
    prompts: Vec<String>,
    generated_images: Vec<String>,
    reference_images: Vec<String>,
    metrics: Option<Vec<String>>,
}

#[derive(Serialize)]
struct GenerationEvalResult {
    clip_score: f64,
    fid: f64,
    inception_score: f64,
    semantic_alignment: f64,
}

fn dummy_similarity(a: &str, b: &str) -> f64 {
    // Simple normalized overlap metric
    let common = a
        .split_whitespace()
        .filter(|w| b.contains(w))
        .count() as f64;
    let denom = (a.split_whitespace().count() + b.split_whitespace().count()) as f64 / 2.0;
    if denom == 0.0 { 0.0 } else { common / denom }
}

#[pyfunction]
pub fn multimodal_evaluate(input_json: &str) -> PyResult<String> {
    let input: MultimodalEvalInput = serde_json::from_str(input_json)
        .map_err(|e| PyValueError::new_err(format!("Invalid input JSON: {}", e)))?;

    // Compute dummy but consistent metrics for now
    let result = MultimodalEvalResult {
        cross_modal_alignment: 0.93,
        visual_grounding: 0.89,
        multimodal_hallucination: 0.12,
    };

    let result_json = serde_json::to_string(&result)
        .map_err(|e| PyValueError::new_err(format!("Serialization error: {}", e)))?;
    Ok(result_json)
}

#[pyfunction]
pub fn multimodal_evaluate_generation(input_json: &str) -> PyResult<String> {
    let input: GenerationEvalInput = serde_json::from_str(input_json)
        .map_err(|e| PyValueError::new_err(format!("Invalid input JSON: {}", e)))?;

    // Compute toy metrics
    let result = GenerationEvalResult {
        clip_score: 0.87,
        fid: 0.21,
        inception_score: 0.81,
        semantic_alignment: 0.9,
    };

    let result_json = serde_json::to_string(&result)
        .map_err(|e| PyValueError::new_err(format!("Serialization error: {}", e)))?;
    Ok(result_json)
}