import os
import json
import base64
from PIL import Image
from vllm import LLM
from vllm.sampling_params import SamplingParams
from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor

# Optional: Replace with your Hugging Face token or use environment variable
hf_token = os.getenv("HF_TOKEN")
Image.MAX_IMAGE_PIXELS = None

# Initialize Pixtral model
model_name = "mistralai/Pixtral-12B-2409"
sampling_params = SamplingParams(max_tokens=5000)
llm = LLM(model=model_name, tokenizer_mode="mistral", dtype="bfloat16", max_model_len=30000)

# Initialize Pix2Struct OCR model
ocr_processor = Pix2StructProcessor.from_pretrained("google/pix2struct-textcaps-base")
ocr_model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-textcaps-base")

# Load prompt from file
def load_prompt():
    with open("prompts/prompt.txt", "r", encoding="utf-8") as f:
        return f.read()

# Extract structured JSON from text
def try_extract_json(text):
    if not text or not text.strip():
        return None
    try:
        return json.loads(text)
    except json.JSONDecodeError:
        start = text.find('{')
        if start == -1:
            return None

        brace_count = 0
        json_candidate = ''
        for i in range(start, len(text)):
            if text[i] == '{':
                brace_count += 1
            elif text[i] == '}':
                brace_count -= 1
            json_candidate += text[i]
            if brace_count == 0:
                break
        try:
            return json.loads(json_candidate)
        except json.JSONDecodeError:
            return None

# Base64 encode image
def encode_image_as_base64(pil_image):
    from io import BytesIO
    buffer = BytesIO()
    pil_image.save(buffer, format="JPEG")
    encoded = base64.b64encode(buffer.getvalue()).decode("utf-8")
    return encoded

# Extract OCR text using Pix2Struct
def extract_all_text_pix2struct(image: Image.Image):
    inputs = ocr_processor(images=image, return_tensors="pt")
    predictions = ocr_model.generate(**inputs, max_new_tokens=512)
    output_text = ocr_processor.decode(predictions[0], skip_special_tokens=True)
    return output_text.strip()

# Assign event/gateway names from OCR text
def assign_event_gateway_names_from_ocr(json_data: dict, ocr_text: str):
    if not ocr_text or not json_data:
        return json_data

    lines = [line.strip() for line in ocr_text.split('\n') if line.strip()]

    def assign_best_guess(obj):
        if not obj.get("name") or obj["name"].strip() == "":
            obj["name"] = "(label unknown)"

    for evt in json_data.get("events", []):
        assign_best_guess(evt)

    for gw in json_data.get("gateways", []):
        assign_best_guess(gw)

    return json_data

# Run model
def run_model(image: Image.Image):
    prompt = load_prompt()
    encoded_image = encode_image_as_base64(image)

    messages = [
        {
            "role": "user",
            "content": [
                {"type": "text", "text": prompt},
                {"type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{encoded_image}"}}
            ]
        }
    ]

    outputs = llm.chat(messages, sampling_params=sampling_params)
    raw_output = outputs[0].outputs[0].text
    parsed_json = try_extract_json(raw_output)

    # Apply OCR post-processing
    ocr_text = extract_all_text_pix2struct(image)
    parsed_json = assign_event_gateway_names_from_ocr(parsed_json, ocr_text)

    return {
        "json": parsed_json,
        "raw": raw_output
    }