import gradio as gr
import spaces

import os
import torch
import numpy as np
import pandas as pd
from transformers import AutoModelForSequenceClassification
from transformers import AutoTokenizer
import torch.nn.functional as F
from huggingface_hub import HfApi
from collections import defaultdict

from label_dicts import (CAP_MEDIA_NUM_DICT, CAP_MEDIA_LABEL_NAMES,
                        CAP_MIN_NUM_DICT, CAP_MIN_LABEL_NAMES,
                        CAP_MIN_MEDIA_NUM_DICT)

from .utils import is_disk_full, release_model

HF_TOKEN = os.environ["hf_read"]

languages = [
    "Multilingual",
]

domains = {
    "media": "media"
}

NUM_TOP_CLASSES = 5
CAP_MEDIA_CODES = list(CAP_MEDIA_NUM_DICT.values())
CAP_MIN_CODES = list(CAP_MIN_NUM_DICT.values())

major_index_to_id = {i: code for i, code in enumerate(CAP_MEDIA_CODES)}
minor_id_to_index = {code: i for i, code in enumerate(CAP_MIN_CODES)}
minor_index_to_id = {i: code for i, code in enumerate(CAP_MIN_CODES)}

major_to_minor_map = defaultdict(list)
for code in CAP_MIN_CODES:
    major_id = int(str(code)[:-2])
    major_to_minor_map[major_id].append(code)
major_to_minor_map = dict(major_to_minor_map)


def normalize_probs(probs: dict) -> dict:
    total = sum(probs.values())
    return {k: v / total for k, v in probs.items()}


def check_huggingface_path(checkpoint_path: str):
    try:
        hf_api = HfApi(token=HF_TOKEN)
        hf_api.model_info(checkpoint_path, token=HF_TOKEN)
        return True
    except:
        return False

def build_huggingface_path(language: str, domain: str, hierarchical: bool):
    if hierarchical:
        return ("poltextlab/xlm-roberta-large-pooled-cap-media", "poltextlab/xlm-roberta-large-pooled-cap-minor-v3")
    else:
        return "poltextlab/xlm-roberta-large-pooled-cap-media-minor"

#@spaces.GPU(duration=30)
def predict(text, major_model_id, minor_model_id, tokenizer_id, HF_TOKEN=None):
    device = torch.device("cpu")

    # Load major and minor models + tokenizer
    major_model = AutoModelForSequenceClassification.from_pretrained(
        major_model_id,
        low_cpu_mem_usage=True,
        device_map="auto",
        offload_folder="offload",
        token=HF_TOKEN
    ).to(device)

    minor_model = AutoModelForSequenceClassification.from_pretrained(
        minor_model_id,
        low_cpu_mem_usage=True,
        device_map="auto",
        offload_folder="offload",
        token=HF_TOKEN
    ).to(device)

    tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)

    # Tokenize input
    inputs = tokenizer(text, max_length=256, truncation=True, padding="do_not_pad", return_tensors="pt").to(device)

    # Predict major topic
    major_model.eval()
    with torch.no_grad():
        major_logits = major_model(**inputs).logits
        major_probs = F.softmax(major_logits, dim=-1)
    major_probs_np = major_probs.cpu().numpy().flatten()
    top_major_index = int(np.argmax(major_probs_np))
    top_major_id = major_index_to_id[top_major_index]

    # Default: show major topic predictions
    filtered_probs = {
        i: float(major_probs_np[i])
        for i in np.argsort(major_probs_np)[::-1]
    }
    filtered_probs = normalize_probs(filtered_probs)
        
    output_pred = {
        f"[{major_index_to_id[k]}] {CAP_MEDIA_LABEL_NAMES[major_index_to_id[k]]}": v
        for k, v in sorted(filtered_probs.items(), key=lambda item: item[1], reverse=True)
    }

    # If eligible for minor prediction
    if top_major_id in major_to_minor_map:
        valid_minor_ids = major_to_minor_map[top_major_id]
        minor_model.eval()
        with torch.no_grad():
            minor_logits = minor_model(**inputs).logits
            minor_probs = F.softmax(minor_logits, dim=-1)
            
        release_model(major_model, major_model_id)
        release_model(minor_model, minor_model_id)
        
        print(minor_probs) # debug
        # Restrict to valid minor codes
        valid_indices = [minor_id_to_index[mid] for mid in valid_minor_ids if mid in minor_id_to_index]
        filtered_probs = {minor_index_to_id[i]: float(minor_probs[0][i]) for i in valid_indices}
        print(filtered_probs) # debug
        filtered_probs = normalize_probs(filtered_probs)
        print(filtered_probs) # debug
        
        output_pred = {
            f"[{top_major_id}] {CAP_MEDIA_LABEL_NAMES[top_major_id]} [{k}] {CAP_MIN_LABEL_NAMES[k]}": v
            for k, v in sorted(filtered_probs.items(), key=lambda item: item[1], reverse=True)
        }

    output_info = f'<p style="text-align: center; display: block">Prediction used <a href="https://huggingface.co/{major_model_id}">{major_model_id}</a> and <a href="https://huggingface.co/{minor_model_id}">{minor_model_id}</a>.</p>'

    interpretation_info = """
    ## How to Interpret These Values (Hierarchical Classification)

    This method returns either:

    - A list of **major (media) topic confidences**, or
    - A list of **minor topic confidences**.

    In the case of minor topics, the values are the confidences for minor topics **within a given major topic**, and they are **normalized to sum to 1**.
    """

    return interpretation_info, output_pred, output_info


def predict_flat(text, model_id, tokenizer_id, HF_TOKEN=None):
    device = torch.device("cpu")

    # Load JIT-traced model
    jit_model_path = f"/data/jit_models/{model_id.replace('/', '_')}.pt"
    model = torch.jit.load(jit_model_path).to(device)
    model.eval()

    # Load tokenizer (still regular HF)
    tokenizer = AutoTokenizer.from_pretrained(tokenizer_id)

    # Tokenize input
    inputs = tokenizer(
        text,
        max_length=256,
        truncation=True,
        padding="do_not_pad",
        return_tensors="pt"
    )
    inputs = {k: v.to(device) for k, v in inputs.items()}

    with torch.no_grad():
        output = model(inputs["input_ids"], inputs["attention_mask"])
        print(output) # debug
        logits = output["logits"]
        
    release_model(model, model_id)

    probs = torch.nn.functional.softmax(logits, dim=1).cpu().numpy().flatten()
    top_indices = np.argsort(probs)[::-1][:10]
    
    CAP_MIN_MEDIA_LABEL_NAMES = CAP_MEDIA_LABEL_NAMES | CAP_MIN_LABEL_NAMES 
    
    output_pred = {}
    for i in top_indices:
        code = CAP_MIN_MEDIA_NUM_DICT[i]
        prob = probs[i]
        
        if code in CAP_MEDIA_LABEL_NAMES:
            # Media (major) topic
            label = CAP_MEDIA_LABEL_NAMES[code]
            display = f"[{code}] {label}"
        else:
            # Minor topic
            major_code = code // 100
            major_label = CAP_MEDIA_LABEL_NAMES[major_code]
            minor_label = CAP_MIN_LABEL_NAMES[code]
            display = f"[{major_code}] {major_label} [{code}] {minor_label}"
        
        output_pred[display] = prob
    
    interpretation_info = """
    ## How to Interpret These Values (Flat Classification)

    This method returns predictions made by a single model. Both media codes and minor topics may appear in the output list. **Only the top 10 most confident labels are displayed**.
    """
    
    output_info = f'<p style="text-align: center; display: block">Prediction was made using the <a href="https://huggingface.co/{model_id}">{model_id}</a> model.</p>'
    
    return interpretation_info, output_pred, output_info


def predict_cap(tmp, method, text, language, domain):
    if is_disk_full():
        os.system('rm -rf /data/models*')
        os.system('rm -r ~/.cache/huggingface/hub')
        
    domain = domains[domain]
    
    if method == "Hierarchical Classification":
        major_model_id, minor_model_id = build_huggingface_path(language, domain, True)
        tokenizer_id = "xlm-roberta-large"
        return predict(text, major_model_id, minor_model_id, tokenizer_id)
        
    else:
        model_id = build_huggingface_path(language, domain, False)
        tokenizer_id = "xlm-roberta-large"
        return predict_flat(text, model_id, tokenizer_id)   

description = """
You can choose between two approaches for making predictions:

**1. Hierarchical Classification**  
First, the model predicts a **major topic**. Then, a second model selects the most probable **subtopic** from within that major topic's category.

**2. Flat Classification (single model)**  
A single model directly predicts the most relevant label from all available classes (both media and minor topics).
"""

demo = gr.Interface(
    title="CAP Media/Minor Topics Babel Demo",
    fn=predict_cap,
    inputs=[gr.Markdown(description),
            gr.Radio(
                choices=["Hierarchical Classification", "Flat Classification"],
                label="Prediction Mode",
                value="Hierarchical Classification"
            ),
            gr.Textbox(lines=6, label="Input"),
            gr.Dropdown(languages, label="Language", value=languages[0]),
            gr.Dropdown(domains.keys(), label="Domain", value=list(domains.keys())[0])],
    outputs=[gr.Markdown(), gr.Label(label="Output"), gr.Markdown()])