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Update app.py
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app.py
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from fastapi import FastAPI, File, UploadFile, Form
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from typing import List
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import pdfplumber
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import pytesseract
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from PIL import Image
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import easyocr
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import docx
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import openpyxl
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from pptx import Presentation
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from transformers import pipeline
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import io
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app = FastAPI()
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# Load Hugging Face models
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qa_pipeline = pipeline("question-answering", model="deepset/roberta-base-squad2")
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vqa_pipeline = pipeline("image-to-text", model="Salesforce/blip-vqa-base") # For images
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def extract_text_from_pdf(pdf_file):
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text = ""
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with pdfplumber.open(pdf_file) as pdf:
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for page in pdf.pages:
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text += page.extract_text() + "\n"
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return text.strip()
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def extract_text_from_docx(docx_file):
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doc = docx.Document(docx_file)
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return "\n".join([para.text for para in doc.paragraphs])
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def extract_text_from_pptx(pptx_file):
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ppt = Presentation(pptx_file)
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text = []
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for slide in ppt.slides:
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for shape in slide.shapes:
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if hasattr(shape, "text"):
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text.append(shape.text)
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return "\n".join(text)
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def extract_text_from_excel(excel_file):
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wb = openpyxl.load_workbook(excel_file)
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text = []
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for sheet in wb.worksheets:
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for row in sheet.iter_rows(values_only=True):
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text.append(" ".join(map(str, row)))
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return "\n".join(text)
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def extract_text_from_image(image_file):
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reader = easyocr.Reader(["en"])
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result = reader.readtext(image_file)
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return " ".join([res[1] for res in result])
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@app.post("/qa/document/")
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async def qa_document(file: UploadFile = File(...), question: str = Form(...)):
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file_ext = file.filename.split(".")[-1].lower()
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if file_ext == "pdf":
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text = extract_text_from_pdf(io.BytesIO(await file.read()))
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elif file_ext == "docx":
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text = extract_text_from_docx(io.BytesIO(await file.read()))
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elif file_ext == "pptx":
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text = extract_text_from_pptx(io.BytesIO(await file.read()))
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elif file_ext == "xlsx":
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text = extract_text_from_excel(io.BytesIO(await file.read()))
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else:
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return {"error": "Unsupported file format!"}
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if not text:
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return {"error": "No text extracted from the document."}
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response = qa_pipeline(question=question, context=text)
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return {"question": question, "answer": response["answer"]}
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@app.post("/qa/image/")
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async def qa_image(file: UploadFile = File(...), question: str = Form(...)):
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image = Image.open(io.BytesIO(await file.read()))
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image_text = extract_text_from_image(image)
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if not image_text:
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return {"error": "No text detected in the image."}
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response = qa_pipeline(question=question, context=image_text)
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return {"question": question, "answer": response["answer"]}
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