import gradio as gr
import evaluate


l3score = evaluate.load("nhop/L3Score")

def compute_l3score(api_key, provider, model, questions, predictions, references):
    try:
        result = l3score.compute(
            questions=[q.strip() for q in questions.split("\n") if q.strip()],
            predictions=[p.strip() for p in predictions.split("\n") if p.strip()],
            references=[r.strip() for r in references.split("\n") if r.strip()],
            api_key=api_key,
            provider=provider,
            model=model
        )
        return result
    except Exception as e:
        return {"error": str(e)}

with gr.Blocks() as demo:
    gr.Markdown(r"""
    # Metric: L3Score
    """)


    with gr.Row():
        api_key = gr.Textbox(label="API Key", type="password")
        provider = gr.Dropdown(label="Provider", choices=["openai", "deepseek", "xai"], value="openai")
        model = gr.Textbox(label="Model", value="gpt-4o-mini")

    with gr.Row():
        questions = gr.Textbox(label="Questions (one per line)", lines=4, placeholder="What is the capital of France?")
        predictions = gr.Textbox(label="Predictions (one per line)", lines=4, placeholder="Paris")
        references = gr.Textbox(label="References (one per line)", lines=4, placeholder="Paris")

    compute_button = gr.Button("Compute L3Score")
    output = gr.JSON(label="L3Score Result")

    compute_button.click(
        fn=compute_l3score,
        inputs=[api_key, provider, model, questions, predictions, references],
        outputs=output
    )

    gr.Markdown(r"""

    ## 📌 Description
    **L3Score** evaluates how semantically close a model-generated answer is to a reference answer for a given question. It prompts a **language model as a judge** using:

    ```text
    You are given a question, ground-truth answer, and a candidate answer.
    
    Question: {{question}}  
    Ground-truth answer: {{gt}}  
    Candidate answer: {{answer}}

    Is the semantic meaning of the ground-truth and candidate answers similar?  
    Answer in one word - Yes or No.
    ```

    The model's **log-probabilities** for "Yes" and "No" tokens are used to compute the score.

    ---""")

    gr.Markdown("""     ## 🧮 Scoring Logic""")
    gr.Markdown(r"""Let $l_{\text{yes}}$ and $l_{\text{no}}$ be the log-probabilities of 'Yes' and 'No', respectively.""",latex_delimiters=[ {"left": "$", "right": "$", "display": False }])
    gr.Markdown(r"""
    - If neither token is in the top-5:

    $$
    \text{L3Score} = 0
    $$

    - If both are present:

    $$
    \text{L3Score} = \frac{\exp(l_{\text{yes}})}{\exp(l_{\text{yes}}) + \exp(l_{\text{no}})}
    $$

    - If only one is present, the missing token’s probability is estimated using the minimum of:
        - remaining probability mass apart from the top-5 tokens
        - the least likely top-5 token

    The score ranges from 0 to 1, where 1 indicates the highest confidence by the LLM that the predicted and reference answers are semantically equivalent.
    See [SPIQA paper](https://arxiv.org/pdf/2407.09413) for details.
                
    ---

    ## 🚀 How to Use

    ```python
    import evaluate

    l3score = evaluate.load("nhop/L3Score")

    questions = ["What is the capital of France?", "What is the capital of Germany?"]
    predictions = ["Paris", "Moscow"]
    references = ["Paris", "Berlin"]

    score = l3score.compute(
        questions=questions,
        predictions=predictions,
        references=references,
        api_key="your-openai-api-key",
        provider="openai",
        model="gpt-4o-mini"
    )

    print(score)
    # {'L3Score': 0.49..., 'Cost': ...}
    ```

    ---

    ## 🔠 Inputs
    | Name         | Type         | Description                                                                 |
    |--------------|--------------|-----------------------------------------------------------------------------|
    | `questions`  | `list[str]`  | The list of input questions.                                                |
    | `predictions`| `list[str]`  | Generated answers by the model being evaluated.                            |
    | `references` | `list[str]`  | Ground-truth or reference answers.                                         |
    | `api_key`    | `str`        | API key for the selected LLM provider.                                     |
    | `provider`   | `str`        | Must support top-n token log-probabilities. **Default**: openai                               |
    | `model`      | `str`        | Name of the evaluation LLM. **Default**: gpt-4o-mini                                               |

    ## 📄 Output
    
    Calling the `compute` method returns a dictionary containing the L3Score:
                
    ```python
    {"L3Score": float, "Cost": float}
    ```
    The value is the **average score** over all (question, prediction, reference) triplets and the total cost of all API calls.

    ---
                
    ## 📊 Examples
                
    ```python
    l3score = evaluate.load("nhop/L3Score")

    score = l3score.compute(
        questions=["What is the capital of France?"],
        predictions=["Paris"],
        references=["Paris"],
        api_key="your-openai-api-key",
        provider="openai",
        model="gpt-4o-mini"
    )
    # {'L3Score': 0.99..., 'Cost': ...}

    score = l3score.compute(
        questions=["What is the capital of Germany?"],
        predictions=["Moscow"],
        references=["Berlin"],
        api_key="your-openai-api-key",
        provider="openai",
        model="gpt-4o-mini"
    )
    # {'L3Score': 0.00..., 'Cost': ...}
    ```
    ---

    ## ⚠️ Limitations and Bias
    - Requires models that expose **top-n token log-probabilities** (e.g., OpenAI, DeepSeek, Groq).
    - Scores are **only comparable when using the same judge model**.

                
    ## 📖 Citation
    ```bibtex
    @article{pramanick2024spiqa,
      title={SPIQA: A Dataset for Multimodal Question Answering on Scientific Papers},
      author={Pramanick, Shraman and Chellappa, Rama and Venugopalan, Subhashini},
      journal={arXiv preprint arXiv:2407.09413},
      year={2024}
    }
    ```
    """)


demo.launch()