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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="/")
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