Delete app.py

app.py

@@ -1,117 +0,0 @@
-import os
-import tempfile
-import fitz  # PyMuPDF
-import streamlit as st
-import torch
-from transformers import AutoConfig, AutoTokenizer, T5ForConditionalGeneration
-
-# Page Configuration
-st.set_page_config(
-    page_title="RTI & Civic PDF Simplifier",
-    page_icon="📄",
-    layout="centered",
-)
-
-st.title("📄 Civic & RTI PDF Simplifier")
-st.markdown(
-    "Upload bureaucratic, legal, or RTI PDF documents to generate concise,"
-    " plain-language bullet points."
-)
-
-MODEL_REPO = "CaseLoop/pdf"
-FALLBACK_BASE = "google/flan-t5-base"
-
-
-@st.cache_resource(show_spinner="Loading model weights...")
-def load_model_and_tokenizer():
-  # Tokenizer fallback
-  try:
-    tokenizer = AutoTokenizer.from_pretrained(MODEL_REPO, legacy=False)
-  except Exception:
-    tokenizer = AutoTokenizer.from_pretrained(FALLBACK_BASE, legacy=False)
-
-  # Model loader with architecture fallback
-  try:
-    model = T5ForConditionalGeneration.from_pretrained(MODEL_REPO)
-  except Exception:
-    try:
-      config = AutoConfig.from_pretrained(FALLBACK_BASE)
-      model = T5ForConditionalGeneration.from_pretrained(
-          MODEL_REPO, config=config
-      )
-    except Exception:
-      model = T5ForConditionalGeneration.from_pretrained(FALLBACK_BASE)
-
-  model.eval()
-  return tokenizer, model
-
-
-tokenizer, model = load_model_and_tokenizer()
-
-
-def extract_chunks(
-    pdf_path: str, chunk_size: int = 180, max_chunks: int = 6
-) -> list[str]:
-  """Extracts lines from a PDF and chunks words for FLAN-T5 input limits."""
-  doc = fitz.open(pdf_path)
-  lines = []
-  for page in doc:
-    text = page.get_text("text")
-    lines.extend([
-        line.strip()
-        for line in text.split("\n")
-        if len(line.strip().split()) >= 5
-    ])
-
-  words = " ".join(lines).split()
-  if not words:
-    return []
-
-  chunks = [
-      " ".join(words[i : i + chunk_size])
-      for i in range(0, len(words), chunk_size)
-  ]
-  return chunks[:max_chunks]
-
-
-# UI File Uploader
-uploaded_file = st.file_uploader(
-    "Choose a PDF file", type=["pdf"], help="Upload an RTI reply or civic notice"
-)
-
-if uploaded_file is not None:
-  if st.button("Simplify Document", type="primary"):
-    with st.spinner("Parsing document and running simplification..."):
-      with tempfile.NamedTemporaryFile(delete=False, suffix=".pdf") as tmp:
-        tmp.write(uploaded_file.read())
-        tmp_path = tmp.name
-
-      chunks = extract_chunks(tmp_path)
-      if os.path.exists(tmp_path):
-        os.remove(tmp_path)
-
-      if not chunks:
-        st.warning(
-            "No extractable text found in this PDF. It might be a scanned image."
-        )
-      else:
-        st.subheader("Key Takeaways in Plain Language:")
-        for idx, chunk in enumerate(chunks, 1):
-          prompt = f"simplify bureaucratic text into simple language: {chunk}"
-          inputs = tokenizer(
-              prompt, return_tensors="pt", max_length=512, truncation=True
-          )
-
-          with torch.no_grad():
-            output_tokens = model.generate(
-                **inputs,
-                max_length=120,
-                min_length=20,
-                num_beams=4,
-                no_repeat_ngram_size=3,
-                repetition_penalty=2.5,
-                early_stopping=True,
-            )
-
-          summary = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
-          st.markdown(f"**{idx}.** {summary}")