from fastapi import FastAPI
from fastapi.responses import RedirectResponse
import fitz  # PyMuPDF
import docx
import openpyxl
import pptx
import io
from PIL import Image

import gradio as gr
from transformers import pipeline

# Models
summarizer = pipeline("summarization", model="sshleifer/distilbart-cnn-12-6")
image_captioner = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")

app = FastAPI()

# -------------------------
# Document Extraction Utils
# -------------------------
def extract_text_from_pdf(file_bytes):
    text = ""
    with fitz.open(stream=file_bytes, filetype="pdf") as doc:
        for page in doc:
            text += page.get_text()
    return text

def extract_text_from_docx(file_bytes):
    doc = docx.Document(io.BytesIO(file_bytes))
    return "\n".join([para.text for para in doc.paragraphs if para.text.strip()])

def extract_text_from_pptx(file_bytes):
    text = []
    prs = pptx.Presentation(io.BytesIO(file_bytes))
    for slide in prs.slides:
        for shape in slide.shapes:
            if hasattr(shape, "text"):
                text.append(shape.text)
    return "\n".join(text)

def extract_text_from_xlsx(file_bytes):
    wb = openpyxl.load_workbook(io.BytesIO(file_bytes))
    text = []
    for sheet in wb.sheetnames:
        ws = wb[sheet]
        for row in ws.iter_rows(values_only=True):
            line = " ".join(str(cell) for cell in row if cell)
            text.append(line)
    return "\n".join(text)

def summarize_document(file):
    file_bytes = file.read()
    filename = getattr(file, "name", "").lower()

    if filename.endswith(".pdf"):
        text = extract_text_from_pdf(file_bytes)
    elif filename.endswith(".docx"):
        text = extract_text_from_docx(file_bytes)
    elif filename.endswith(".pptx"):
        text = extract_text_from_pptx(file_bytes)
    elif filename.endswith(".xlsx"):
        text = extract_text_from_xlsx(file_bytes)
    else:
        return "❌ Unsupported file format."

    if not text.strip():
        return "❗ No extractable text found."

    try:
        summary = summarizer(text[:3000], max_length=150, min_length=30, do_sample=False)
        return f"📄 Summary:\n{summary[0]['summary_text']}"
    except Exception as e:
        return f"⚠️ Summarization error: {e}"

def interpret_image(image):
    if image is None:
        return "No image uploaded."
    try:
        return f"🖼️ Caption:\n{image_captioner(image)[0]['generated_text']}"
    except Exception as e:
        return f"⚠️ Image captioning error: {e}"

# -------------------------
# Gradio Interfaces
# -------------------------
doc_summary = gr.Interface(
    fn=summarize_document,
    inputs=gr.File(label="Upload a Document"),
    outputs="text",
    title="📄 Document Summarizer"
)

img_caption = gr.Interface(
    fn=interpret_image,
    inputs=gr.Image(type="pil", label="Upload an Image"),
    outputs="text",
    title="🖼️ Image Interpreter"
)

# -------------------------
# Combine into Gradio + FastAPI
# -------------------------
demo = gr.TabbedInterface([doc_summary, img_caption], ["Document Summary", "Image Captioning"])
app = gr.mount_gradio_app(app, demo, path="/")

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