Update app.py

app.py

@@ -1,90 +1,39 @@
+# quicknote_app.py
 import streamlit as st
-from io import BytesIO
-import PyPDF2
-import docx2txt
-from PIL import Image
-import torch
-from transformers import AutoProcessor, AutoModelForImageClassification
+import os
+import json
 
-st.set_page_config(page_title="📝 Note Input", layout="centered")
-st.title("📄 Type of Text Input")
+st.set_page_config(page_title="QuickNote Clone", layout="wide")
 
-def extract_pdf_text(uploaded_file):
-    reader = PyPDF2.PdfReader(uploaded_file)
-    text = ""
-    for page in reader.pages:
-        page_text = page.extract_text()
-        if page_text:
-            text += page_text + "\n"
-    return text
+# Load note ID from query parameter (optional)
+query_params = st.experimental_get_query_params()
+note_id = query_params.get("note", ["default"])[0]
 
-def format_single_line(text: str) -> str:
-    # split on any whitespace and re-join with single spaces
-    return " ".join(text.split())
+NOTE_DIR = "notes"
+os.makedirs(NOTE_DIR, exist_ok=True)
+note_file_path = os.path.join(NOTE_DIR, f"{note_id}.json")
 
-def extract_docx_text(uploaded_file):
-    # docx2txt.process accepts a path or a file-like object
-    return docx2txt.process(uploaded_file)
-# --- Image model setup ---
-OCR_MODEL = "microsoft/trocr-base-printed"
-
-
-@st.cache_resource
-def load_ocr_model():
-    processor = TrOCRProcessor.from_pretrained(OCR_MODEL)
-    model     = VisionEncoderDecoderModel.from_pretrained(OCR_MODEL)
-    return processor, model
-
-ocr_processor, ocr_model = load_ocr_model()
-# --- Main UI ---
-input_type = st.selectbox(
-    "Select the type of input:",
-    ["Select...", "PDF", "Word Document", "Text", "Image"]
-)
-
-if input_type == "PDF":
-    uploaded_file = st.file_uploader("Upload a PDF file", type=["pdf"])
-    if uploaded_file is not None:
-        raw_text = extract_pdf_text(uploaded_file)
-        single_line = format_single_line(raw_text)
-        st.subheader("📜 PDF Text as One Line")
-        st.text_area("All content in one line", single_line, height=150)
-
-elif input_type == "Word Document":
-    uploaded_file = st.file_uploader("Upload a DOCX file", type=["docx"])
-    if uploaded_file is not None:
-        extracted_text = extract_docx_text(uploaded_file)
-        st.subheader("📜 Extracted DOCX Text")
-        st.text_area("Content", extracted_text, height=300)
-
-elif input_type == "Text":
-    notes = st.text_area(
-        "Paste your class notes here",
-        height=300,
-        placeholder="e.g., AI notes ..."
-    )
-    if notes:
-        st.subheader("📝 Your Input Text")
-        st.text_area("Content", notes, height=300)
-
-elif input_type == "Image":
-    uploaded_img = st.file_uploader("Upload a PNG/JPG image", type=["png", "jpg", "jpeg"])
-    if uploaded_img is not None:
-        img = Image.open(uploaded_img).convert("RGB")
-        st.image(img, caption="🖼️ Uploaded Image", use_column_width=True)
-
-        # 1. Preprocess for OCR
-        pixel_values = ocr_processor(images=img, return_tensors="pt").pixel_values
-
-        # 2. Generate and decode
-        with torch.no_grad():
-            generated_ids = ocr_model.generate(pixel_values)
-        extracted_text = ocr_processor.batch_decode(generated_ids, skip_special_tokens=True)[0]
+# Load existing note if available
+if os.path.exists(note_file_path):
+    with open(note_file_path, "r", encoding="utf-8") as f:
+        note_data = json.load(f)
+    note_text = note_data.get("text", "")
+else:
+    note_text = ""
 
-        st.subheader("🖋️ Extracted Text from Image")
-        st.text_area("OCR Result", extracted_text, height=300)
+st.title("📝 QuickNote Clone")
+st.caption("Clean text interface built with Streamlit.")
 
-else:
-    st.info("Please select an input type to get started.")
+# Text editor
+text_input = st.text_area("Your Note", value=note_text, height=500, label_visibility="collapsed")
 
+# Save note manually
+if st.button("💾 Save Note"):
+    with open(note_file_path, "w", encoding="utf-8") as f:
+        json.dump({"text": text_input}, f)
+    st.success("Note saved!")
 
+# Autosave every time user types (optional)
+if text_input != note_text:
+    with open(note_file_path, "w", encoding="utf-8") as f:
+        json.dump({"text": text_input}, f)