# inference_api.py
import os
import fitz  # PyMuPDF
import fasttext
import torch
from PIL import Image
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
from IndicTransToolkit.processor import IndicProcessor
import google.generativeai as genai
from fastapi import FastAPI
from pydantic import BaseModel
from typing import Optional
import json

app = FastAPI()

# === CONFIGURATION ===
GEMINI_API_KEY = os.environ.get("GEMINI_API_KEY")
TRANSLATION_MODEL_REPO_ID = "ai4bharat/indictrans2-indic-en-1B"
OCR_MODEL_ID = "microsoft/trocr-base-printed"
LANGUAGE_TO_TRANSLATE = "mal"
DEVICE = "cpu"

# --- Configure Gemini ---
if GEMINI_API_KEY:
    genai.configure(api_key=GEMINI_API_KEY)
else:
    print("🔴 GEMINI_API_KEY not set.")

# --- Load Models ---
translation_tokenizer = AutoTokenizer.from_pretrained(TRANSLATION_MODEL_REPO_ID, trust_remote_code=True)
translation_model = AutoModelForSeq2SeqLM.from_pretrained(
    TRANSLATION_MODEL_REPO_ID, trust_remote_code=True, torch_dtype=torch.float32
).to(DEVICE)
ip = IndicProcessor(inference=True)

ft_model_path = hf_hub_download(repo_id="facebook/fasttext-language-identification", filename="model.bin")
lang_detect_model = fasttext.load_model(ft_model_path)

ocr_pipeline = pipeline("image-to-text", model=OCR_MODEL_ID, device=-1)

# === HELPER FUNCTIONS ===
def classify_image_with_gemini(image: Image.Image):
    model = genai.GenerativeModel('gemini-1.5-flash-latest')
    prompt = "Is this image primarily a text document or an engineering/technical diagram? Answer with only 'document' or 'diagram'."
    response = model.generate_content([prompt, image])
    classification = response.text.strip().lower()
    return "diagram" if "diagram" in classification else "document"

def summarize_diagram_with_gemini(image: Image.Image):
    model = genai.GenerativeModel('gemini-1.5-flash-latest')
    prompt = "Describe the contents of this technical diagram in a concise summary."
    response = model.generate_content([prompt, image])
    return response.text.strip()

def extract_text_from_image(path):
    image = Image.open(path).convert("RGB")
    image_type = classify_image_with_gemini(image)
    if image_type == "diagram":
        return summarize_diagram_with_gemini(image)
    else:
        out = ocr_pipeline(image)
        return out[0]["generated_text"] if out else ""

def extract_text_from_pdf(path):
    doc = fitz.open(path)
    return "".join(page.get_text("text") + "\n" for page in doc)

def read_text_from_txt(path):
    with open(path, "r", encoding="utf-8") as f:
        return f.read()

def detect_language(text_snippet):
    s = text_snippet.replace("\n", " ").strip()
    if not s: return None
    preds = lang_detect_model.predict(s, k=1)
    return preds[0][0].split("__")[-1] if preds and preds[0] else None

def translate_chunk(chunk):
    batch = ip.preprocess_batch([chunk], src_lang="mal_Mlym", tgt_lang="eng_Latn")
    inputs = translation_tokenizer(batch, return_tensors="pt", padding=True, truncation=True, max_length=512).to(DEVICE)
    with torch.no_grad():
        generated_tokens = translation_model.generate(**inputs, num_beams=5, max_length=512, early_stopping=True)
    decoded = translation_tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)
    return ip.postprocess_batch(decoded, lang="eng_Latn")[0]

def generate_structured_json(text_to_analyze):
    model = genai.GenerativeModel('gemini-1.5-flash-latest')
    prompt = f"Analyze this document and extract key info as JSON: {text_to_analyze}"
    json_schema = {
        "type": "OBJECT",
        "properties": {
            "summary": {"type": "STRING"},
            "actions_required": {"type": "ARRAY", "items": {
                "type": "OBJECT",
                "properties": {"action": {"type": "STRING"}, "priority": {"type": "STRING", "enum": ["High","Medium","Low"]}, "deadline": {"type": "STRING"}, "notes": {"type": "STRING"}},
                "required": ["action","priority","deadline","notes"]
            }},
            "departments_to_notify": {"type": "ARRAY", "items": {"type": "STRING"}},
            "cross_document_flags": {"type": "ARRAY", "items": {
                "type": "OBJECT",
                "properties": {"related_document_type": {"type": "STRING"}, "related_issue": {"type": "STRING"}},
                "required": ["related_document_type","related_issue"]
            }}
        },
        "required": ["summary","actions_required","departments_to_notify","cross_document_flags"]
    }
    generation_config = genai.types.GenerationConfig(response_mime_type="application/json", response_schema=json_schema)
    response = model.generate_content(prompt, generation_config=generation_config)
    return json.loads(response.text)

def check_relevance_with_gemini(summary_text):
    model = genai.GenerativeModel('gemini-1.5-flash-latest')
    prompt = f'Is this summary relevant to transportation, infrastructure, railways, or metro systems? Answer "Yes" or "No". Summary: {summary_text}'
    response = model.generate_content(prompt)
    return "yes" in response.text.strip().lower()

# === API INPUT SCHEMA ===
class InputFile(BaseModel):
    file_path: str

@app.post("/predict")
def predict(file: InputFile):
    if not GEMINI_API_KEY:
        return {"error": "Gemini API key not set."}
    path = file.file_path
    ext = os.path.splitext(path)[1].lower()
    
    # Phase 1: Extract text
    if ext == ".pdf":
        original_text = extract_text_from_pdf(path)
    elif ext == ".txt":
        original_text = read_text_from_txt(path)
    elif ext in [".png", ".jpg", ".jpeg"]:
        original_text = extract_text_from_image(path)
    else:
        return {"error": "Unsupported file type."}
    
    # Phase 2: Translate Malayalam if detected
    lines = original_text.split("\n")
    translated_lines = []
    for ln in lines:
        if not ln.strip(): continue
        lang = detect_language(ln)
        if lang == LANGUAGE_TO_TRANSLATE:
            translated_lines.append(translate_chunk(ln))
        else:
            translated_lines.append(ln)
    final_text = "\n".join(translated_lines)
    
    # Phase 3: Gemini analysis
    summary_data = generate_structured_json(final_text)
    if not summary_data or "summary" not in summary_data:
        return {"error": "Failed to generate analysis."}
    
    is_relevant = check_relevance_with_gemini(summary_data["summary"])
    if is_relevant:
        return summary_data
    else:
        return {"status": "Not Applicable", "reason": "Document not relevant to KMRL."}