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import os
import io
from fastapi import FastAPI, UploadFile, File, Form
from fastapi.middleware.cors import CORSMiddleware
from fastapi.responses import JSONResponse, HTMLResponse
from huggingface_hub import InferenceClient
from PyPDF2 import PdfReader
from docx import Document
from PIL import Image
from io import BytesIO
# Load Hugging Face Token securely
HUGGINGFACE_TOKEN = os.getenv("HF_TOKEN")
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
# Initialize Hugging Face clients
summary_client = InferenceClient(model="facebook/bart-large-cnn", token=HUGGINGFACE_TOKEN)
qa_client = InferenceClient(model="deepset/roberta-base-squad2", token=HUGGINGFACE_TOKEN)
image_caption_client = InferenceClient(model="nlpconnect/vit-gpt2-image-captioning", token=HUGGINGFACE_TOKEN)
def extract_text_from_pdf(content: bytes) -> str:
reader = PdfReader(io.BytesIO(content))
return "\n".join(page.extract_text() or "" for page in reader.pages).strip()
def extract_text_from_docx(content: bytes) -> str:
doc = Document(io.BytesIO(content))
return "\n".join(para.text for para in doc.paragraphs).strip()
def process_uploaded_file(file: UploadFile) -> str:
content = file.file.read()
extension = file.filename.split('.')[-1].lower()
if extension == "pdf":
return extract_text_from_pdf(content)
elif extension == "docx":
return extract_text_from_docx(content)
elif extension == "txt":
return content.decode("utf-8").strip()
else:
raise ValueError("Unsupported file type.")
@app.get("/", response_class=HTMLResponse)
async def serve_homepage():
with open("index.html", "r", encoding="utf-8") as f:
return HTMLResponse(content=f.read(), status_code=200)
@app.post("/api/summarize")
async def summarize_document(file: UploadFile = File(...)):
try:
text = process_uploaded_file(file)
if len(text) < 20:
return {"result": "Document too short to summarize."}
summary = summary_client.summarization(text[:3000])
return {"result": summary}
except Exception as e:
return JSONResponse(status_code=500, content={"error": str(e)})
@app.post("/api/caption")
async def caption_image(file: UploadFile = File(...)):
try:
image_bytes = await file.read()
image_pil = Image.open(io.BytesIO(image_bytes)).convert("RGB")
image_pil.thumbnail((1024, 1024))
img_byte_arr = BytesIO()
image_pil.save(img_byte_arr, format='JPEG')
img_byte_arr = img_byte_arr.getvalue()
result = image_caption_client.image_to_text(img_byte_arr)
if isinstance(result, dict):
caption = result.get("generated_text") or result.get("caption") or "No caption found."
elif isinstance(result, list) and result:
caption = result[0].get("generated_text", "No caption found.")
elif isinstance(result, str):
caption = result
else:
caption = "No caption found."
return {"result": caption}
except Exception as e:
return JSONResponse(status_code=500, content={"error": str(e)})
@app.post("/api/qa")
async def question_answering(file: UploadFile = File(...), question: str = Form(...)):
try:
content_type = file.content_type
if content_type.startswith("image/"):
image_bytes = await file.read()
image_pil = Image.open(io.BytesIO(image_bytes)).convert("RGB")
image_pil.thumbnail((1024, 1024))
img_byte_arr = BytesIO()
image_pil.save(img_byte_arr, format='JPEG')
img_byte_arr = img_byte_arr.getvalue()
result = image_caption_client.image_to_text(img_byte_arr)
context = result.get("generated_text") if isinstance(result, dict) else result
else:
text = process_uploaded_file(file)
if len(text) < 20:
return {"result": "Document too short to answer questions."}
context = text[:3000]
if not context:
return {"result": "No context available to answer."}
answer = qa_client.question_answering(question=question, context=context)
return {"result": answer.get("answer", "No answer found.")}
except Exception as e:
return JSONResponse(status_code=500, content={"error": str(e)})
if __name__ == "__main__":
import uvicorn
uvicorn.run("main:app", host="0.0.0.0", port=8000, reload=True)
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