from fastapi import FastAPI, File, UploadFile
import fitz  # PyMuPDF for PDF parsing
from tika import parser  # Apache Tika for document parsing
import openpyxl
from pptx import Presentation
import torch
from torchvision import transforms
from torchvision.models.detection import fasterrcnn_resnet50_fpn
from PIL import Image
from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
import gradio as gr
from fastapi.responses import RedirectResponse
import numpy as np
import easyocr
import os
# Initialize FastAPI
app = FastAPI()

print(f"🔄 Loading models")
model_name = "mistralai/Mistral-7B-Instruct"
hf_token = os.getenv("HUGGINGFACEHUB_API_TOKEN")  # Fetch token from environment variable

tokenizer = AutoTokenizer.from_pretrained(model_name, token=hf_token)
model = AutoModelForCausalLM.from_pretrained(model_name, token=hf_token)

doc_qa_pipeline = pipeline("text-generation", model=model, tokenizer=tokenizer, device=-1)

# Load Image Captioning Model (nlpconnect/vit-gpt2-image-captioning)
image_captioning_pipeline = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")

# Initialize OCR Model (Lazy Load)
reader = easyocr.Reader(["en"], gpu=True)

# Allowed File Extensions
ALLOWED_EXTENSIONS = {"pdf", "docx", "pptx", "xlsx"}

def validate_file_type(file):
    ext = file.name.split(".")[-1].lower()
    print(f"🔍 Validating file type: {ext}")
    if ext not in ALLOWED_EXTENSIONS:
        return f"❌ Unsupported file format: {ext}"
    return None

# Function to truncate text to 450 tokens
def truncate_text(text, max_tokens=450):
    words = text.split()
    truncated = " ".join(words[:max_tokens])
    print(f"✂️ Truncated text to {max_tokens} tokens.")
    return truncated

# Document Text Extraction Functions
def extract_text_from_pdf(pdf_file):
    try:
        print("📄 Extracting text from PDF...")
        doc = fitz.open(pdf_file)
        text = "\n".join([page.get_text("text") for page in doc])
        return text if text else "⚠️ No text found."
    except Exception as e:
        return f"❌ Error reading PDF: {str(e)}"

def extract_text_with_tika(file):
    try:
        print("📝 Extracting text with Tika...")
        parsed = parser.from_buffer(file)
        return parsed.get("content", "⚠️ No text found.").strip()
    except Exception as e:
        return f"❌ Error reading document: {str(e)}"

def extract_text_from_pptx(pptx_file):
    try:
        print("📊 Extracting text from PPTX...")
        ppt = Presentation(pptx_file)
        text = []
        for slide in ppt.slides:
            for shape in slide.shapes:
                if hasattr(shape, "text"):
                    text.append(shape.text)
        return "\n".join(text) if text else "⚠️ No text found."
    except Exception as e:
        return f"❌ Error reading PPTX: {str(e)}"

def extract_text_from_excel(excel_file):
    try:
        print("📊 Extracting text from Excel...")
        wb = openpyxl.load_workbook(excel_file, read_only=True)
        text = []
        for sheet in wb.worksheets:
            for row in sheet.iter_rows(values_only=True):
                text.append(" ".join(map(str, row)))
        return "\n".join(text) if text else "⚠️ No text found."
    except Exception as e:
        return f"❌ Error reading Excel: {str(e)}"

def answer_question_from_document(file, question):
    print("📂 Processing document for QA...")
    validation_error = validate_file_type(file)
    if validation_error:
        return validation_error
    
    file_ext = file.name.split(".")[-1].lower()
    if file_ext == "pdf":
        text = extract_text_from_pdf(file)
    elif file_ext in ["docx", "pptx"]:
        text = extract_text_with_tika(file)
    elif file_ext == "xlsx":
        text = extract_text_from_excel(file)
    else:
        return "❌ Unsupported file format!"
    
    if not text:
        return "⚠️ No text extracted from the document."
    
    truncated_text = truncate_text(text)
    print("🤖 Generating response...")
    response = doc_qa_pipeline(f"Question: {question}\nContext: {truncated_text}")
    
    return response[0]["generated_text"]

def answer_question_from_image(image, question):
    print("🖼️ Generating caption for image...")
    caption = image_captioning_pipeline(image)[0]['generated_text']
    
    print("🤖 Answering question based on caption...")
    response = doc_qa_pipeline(f"Question: {question}\nContext: {caption}")
    
    return response[0]["generated_text"]

# Gradio UI for Document & Image QA
doc_interface = gr.Interface(
    fn=answer_question_from_document,
    inputs=[gr.File(label="📂 Upload Document"), gr.Textbox(label="💬 Ask a Question")],
    outputs="text",
    title="📄 AI Document Question Answering"
)

img_interface = gr.Interface(
    fn=answer_question_from_image,
    inputs=[gr.Image(label="🖼️ Upload Image"), gr.Textbox(label="💬 Ask a Question")],
    outputs="text",
    title="🖼️ AI Image Question Answering"
)

# Mount Gradio Interfaces
demo = gr.TabbedInterface([doc_interface, img_interface], ["📄 Document QA", "🖼️ Image QA"])
app = gr.mount_gradio_app(app, demo, path="/")

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