import os
import sys
import tempfile
import gradio as gr
import requests
import uvicorn
import torch
import base64

from fastapi import FastAPI, File, UploadFile
from fastapi.responses import RedirectResponse, StreamingResponse
from typing import List
from pdf2image import convert_from_bytes
from PIL import Image
from torch.utils.data import DataLoader
from transformers import AutoProcessor
from reportlab.pdfgen import canvas
from reportlab.lib.pagesizes import letter
from io import BytesIO

sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), './colpali-main')))

from colpali_engine.models.paligemma_colbert_architecture import ColPali
from colpali_engine.trainer.retrieval_evaluator import CustomEvaluator
from colpali_engine.utils.colpali_processing_utils import (
    process_images,
    process_queries,
)

app = FastAPI()

# Load model
model_name = "vidore/colpali"
token = os.environ.get("HF_TOKEN")
model = ColPali.from_pretrained(
    "google/paligemma-3b-mix-448", torch_dtype=torch.bfloat16, device_map="cpu", token = token).eval()

model.load_adapter(model_name)
processor = AutoProcessor.from_pretrained(model_name, token = token)
device = "cuda:0" if torch.cuda.is_available() else "cpu"
if device != model.device:
    model.to(device)
mock_image = Image.new("RGB", (448, 448), (255, 255, 255))

# In-memory storage
ds = []
images = []

@app.get("/")
def read_root():
    return RedirectResponse(url="/docs")

@app.post("/index")
async def index(files: List[UploadFile] = File(...)):
    global ds, images
    images = []
    ds = []
    for file in files:
        content = await file.read()
        pdf_image_list = convert_from_bytes(content)
        images.extend(pdf_image_list)

    # Create embeddings for each file and load in memory storage
    dataloader = DataLoader(
        images,
        batch_size=4,
        shuffle=False,
        collate_fn=lambda x: process_images(processor, x),
    )
    for batch_doc in dataloader:
        with torch.no_grad():
            batch_doc = {k: v.to(device) for k, v in batch_doc.items()}
            embeddings_doc = model(**batch_doc)
        ds.extend(list(torch.unbind(embeddings_doc.to("cpu"))))
    
    return {"message": f"Uploaded and converted {len(images)} pages"}

def generate_pdf(results):
    pdf_buffer = BytesIO()
    c = canvas.Canvas(pdf_buffer, pagesize=letter)
    width, height = letter

    for result in results:
        img_base64 = result["image"]
        img_data = base64.b64decode(img_base64)
        
        # Create a temporary file to hold the image
        with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as temp_file:
            temp_file.write(img_data)
            temp_file.flush()
            
            # Draw the image from the temporary file
            c.drawImage(temp_file.name, 0, 0, width, height)
            c.showPage()
        
        # Clean up the temporary file
        os.remove(temp_file.name)

    c.save()
    pdf_buffer.seek(0)
    return pdf_buffer

@app.get("/search")
async def search(query: str, k: int = 1):
    qs = []
    with torch.no_grad():
        batch_query = process_queries(processor, [query], mock_image)
        batch_query = {k: v.to(device) for k, v in batch_query.items()}
        embeddings_query = model(**batch_query)
        qs.extend(list(torch.unbind(embeddings_query.to("cpu"))))

    retriever_evaluator = CustomEvaluator(is_multi_vector=True)
    scores = retriever_evaluator.evaluate(qs, ds)

    top_k_indices = scores.argsort(axis=1)[0][-k:][::-1]

    results = []
    for idx in top_k_indices:
        img_byte_arr = BytesIO()
        images[idx].save(img_byte_arr, format='PNG')
        img_base64 = base64.b64encode(img_byte_arr.getvalue()).decode('utf-8')
        results.append({"image": img_base64, "page": f"Page {idx}"})

    pdf_buffer = generate_pdf(results)

    # Use StreamingResponse to handle in-memory file
    response = StreamingResponse(pdf_buffer, media_type='application/pdf')
    response.headers['Content-Disposition'] = 'attachment; filename="results.pdf"'

    return response

@app.post("/recommendation")
async def recommendation(file: UploadFile = File(...), k: int = 10):
    content = await file.read()
    pdf_image_list = convert_from_bytes(content)

    qs = []
    dataloader = DataLoader(
        pdf_image_list,
        batch_size=4,
        shuffle=False,
        collate_fn=lambda x: process_images(processor, x),
    )
    for batch_query in dataloader:
        with torch.no_grad():
            batch_query = {k: v.to(device) for k, v in batch_query.items()}
            embeddings_query = model(**batch_query)
        qs.extend(list(torch.unbind(embeddings_query.to("cpu"))))

    retriever_evaluator = CustomEvaluator(is_multi_vector=True)
    scores = retriever_evaluator.evaluate(qs, ds)

    top_k_indices = scores.argsort(axis=1)[0][-k-1:-1][::-1]

    results = []
    for idx in top_k_indices:
        img_byte_arr = BytesIO()
        images[idx].save(img_byte_arr, format='PNG')
        img_base64 = base64.b64encode(img_byte_arr.getvalue()).decode('utf-8')
        results.append({"image": img_base64, "page": f"Page {idx}"})

    pdf_buffer = generate_pdf(results)

    response = StreamingResponse(pdf_buffer, media_type='application/pdf')
    response.headers['Content-Disposition'] = 'attachment; filename="results.pdf"'

    return response

if __name__ == "__main__":
    uvicorn.run(app, host="0.0.0.0", port=7860)