Create app.py

app.py

@@ -0,0 +1,117 @@
+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}")