Update app.py

app.py

@@ -1,4 +1,4 @@
-from fastapi import FastAPI, Form, File, UploadFile
+"""from fastapi import FastAPI, Form, File, UploadFile
 from fastapi.responses import RedirectResponse
 from fastapi.staticfiles import StaticFiles
 from pydantic import BaseModel
@@ -99,3 +99,120 @@ async def question_answering_image(question: str = Form(...), image_file: Upload
 @app.get("/docs")
 async def get_docs(request: Request):
     return templates.TemplateResponse("index.html", {"request": request})
+"""
+from fastapi import FastAPI, Form, File, UploadFile
+from fastapi.responses import RedirectResponse
+from fastapi.staticfiles import StaticFiles
+from pydantic import BaseModel
+from transformers import pipeline
+import os
+from PIL import Image
+import io
+import pdfplumber
+import docx
+import openpyxl
+import pytesseract
+from io import BytesIO
+import fitz  # PyMuPDF
+import easyocr
+from fastapi.templating import Jinja2Templates
+from starlette.requests import Request
+from fastapi.middleware.cors import CORSMiddleware
+
+# Initialize the app
+app = FastAPI()
+
+# Enable CORS (for frontend interaction)
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # Adjust this for security
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Mount the static directory to serve HTML, CSS, JS files
+app.mount("/static", StaticFiles(directory="static"), name="static")
+
+# Initialize transformers pipelines
+qa_pipeline = pipeline("question-answering", model="microsoft/phi-2", tokenizer="microsoft/phi-2")
+image_qa_pipeline = pipeline("vqa", model="Salesforce/blip-vqa-base")
+
+# Initialize EasyOCR for image-based text extraction
+reader = easyocr.Reader(['en'])
+
+# Define a template for rendering HTML
+templates = Jinja2Templates(directory="templates")
+
+# Ensure temp_files directory exists
+temp_dir = "temp_files"
+os.makedirs(temp_dir, exist_ok=True)
+
+# Function to process PDFs
+def extract_pdf_text(file_path: str):
+    with pdfplumber.open(file_path) as pdf:
+        text = ""
+        for page in pdf.pages:
+            extracted = page.extract_text()
+            if extracted:
+                text += extracted + "\n"
+    return text
+
+# Function to process DOCX files
+def extract_docx_text(file_path: str):
+    doc = docx.Document(file_path)
+    return "\n".join([para.text for para in doc.paragraphs])
+
+# Function to process PPTX files
+def extract_pptx_text(file_path: str):
+    from pptx import Presentation
+    prs = Presentation(file_path)
+    return "\n".join([shape.text for slide in prs.slides for shape in slide.shapes if hasattr(shape, "text")])
+
+# Function to extract text from images using OCR
+def extract_text_from_image(image: Image):
+    return pytesseract.image_to_string(image)
+
+# Home route
+@app.get("/")
+def home():
+    return RedirectResponse(url="/docs")
+
+# Function to answer questions based on document content
+@app.post("/docs")
+async def question_answering_doc(question: str = Form(...), file: UploadFile = File(...)):
+    file_ext = file.filename.split(".")[-1].lower()
+    file_path = os.path.join(temp_dir, file.filename)
+
+    with open(file_path, "wb") as f:
+        f.write(await file.read())
+
+    if file_ext in ["pdf"]:
+        text = extract_pdf_text(file_path)
+    elif file_ext in ["docx"]:
+        text = extract_docx_text(file_path)
+    elif file_ext in ["pptx"]:
+        text = extract_pptx_text(file_path)
+    else:
+        return {"error": "Unsupported file format"}
+
+    qa_result = qa_pipeline(question=question, context=text)
+    return {"answer": qa_result['answer']}
+
+# Function to answer questions based on images
+@app.post("/images")
+async def question_answering_image(question: str = Form(...), image_file: UploadFile = File(...)):
+    image = Image.open(BytesIO(await image_file.read()))
+
+    # Extract text from image using OCR
+    image_text = extract_text_from_image(image)
+
+    # Get answer from the VQA model
+    image_qa_result = image_qa_pipeline({"image": image, "question": question})
+    
+    return {"answer": image_qa_result[0]['answer'], "image_text": image_text}
+
+# Serve the application in Hugging Face space
+@app.get("/docs")
+async def get_docs(request: Request):
+    return templates.TemplateResponse("index.html", {"request": request})