Update app.py

app.py

@@ -1,17 +1,17 @@
 import os, re
-from typing import List, Optional
-from pydantic import BaseModel
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from typing import List
 import torch
 import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
 
-# -------- Model (CPU) --------
-MODEL_ID = os.getenv("MODEL_ID", "danibor/flan-t5-base-humanizer")
+# ---------------- Model (CPU) ----------------
+# Humaneyes is Pegasus-based (seq2seq).  Loads fine on CPU Spaces.
+MODEL_ID = os.getenv("MODEL_ID", "Eemansleepdeprived/Humaneyes")
 device = "cuda" if torch.cuda.is_available() else "cpu"
 tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
 model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_ID).to(device).eval()
 
-# -------- Protect / restore --------
+# ---------------- Protect / restore ----------------
 SENTINEL_OPEN  = "§§KEEP_OPEN§§"
 SENTINEL_CLOSE = "§§KEEP_CLOSE§§"
 URL_RE  = re.compile(r'(https?://\S+)')
@@ -57,7 +57,7 @@ def build_input(text: str, tone: str, region: str, level: str, intensity: int) -
 
 def chunk_text(s: str, max_chars: int = 1100):
     parts, buf, cur = [], [], 0
-    for block in re.split(r"(\n{2,})", s):
+    for block in re.split(r"(\n{2,})", s):   # keep paragraph gaps
         if cur + len(block) > max_chars and buf:
             parts.append("".join(buf)); buf, cur = [block], len(block)
         else:
@@ -85,13 +85,14 @@ def humanize_core(text: str, tone: str, region: str, reading_level: str, intensi
     outs = [generate_one(ch, max_new=320) for ch in chunks]
     draft = "".join(outs).replace("—", "-").strip()
     final_text = restore(draft, bag)
+    # ensure protected spans survived (paranoid check)
     for i, span in enumerate(bag):
         marker = f"{SENTINEL_OPEN}{i}{SENTINEL_CLOSE}"
         if marker in protected_text and span not in final_text:
             final_text = final_text.replace(marker, span)
     return final_text
 
-# -------- Gradio UI (also enables REST API) --------
+# ---------------- Gradio UI (also exposes REST at /api/predict/) ----------------
 def ui_humanize(text, tone, region, reading_level, intensity):
     return humanize_core(text, tone, region, reading_level, int(intensity))
 
@@ -105,9 +106,6 @@ demo = gr.Interface(
         gr.Slider(1, 10, value=6, step=1, label="Humanization intensity"),
     ],
     outputs=gr.Textbox(label="Humanized"),
-    title="NoteCraft Humanizer (FLAN-T5)",
-    description="Model: danibor/flan-t5-base-humanizer • REST: POST /api/predict/ with { data: [text,tone,region,level,intensity] }",
+    title="NoteCraft Humanizer (Humaneyes)",
+    description="Model: Eemansleepdeprived/Humaneyes (Pegasus). REST: POST /api/predict/ with { data: [text,tone,region,level,intensity] }",
 ).queue()
-
-if __name__ == "__main__":
-    demo.launch()