Upload app.py

app.py

@@ -1,46 +1,16 @@
 import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from authormist import Humanizer
 
-# Load model & tokenizer
-model_name = "authormist/authormist-originality"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForCausalLM.from_pretrained(model_name)
+humanizer = Humanizer()
 
-def humanize_text(sop_text):
-    # 1. Split into paragraphs (double-newline separated)
-    paras = [p.strip() for p in sop_text.split("\n\n") if p.strip()]
-    humanized = []
-
-    for p in paras:
-        # 2. Strong, guiding prompt
-        prompt = (
-            "You are a senior design student helping me rewrite my Statement of Purpose paragraph. "
-            "Make it sound genuine, personal, and conversational—avoid anything too polished or robotic:\n\n"
-            f"{p}\n\nHumanized:"
-        )
-        inputs = tokenizer(prompt, return_tensors="pt")
-        outputs = model.generate(
-            inputs.input_ids,
-            max_new_tokens=256,
-            temperature=0.85,
-            top_p=0.96,
-            do_sample=True
-        )
-        text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-        # 3. Strip off the prompt header and cleanup
-        text = text.replace("Humanized:", "").strip()
-        humanized.append(text)
-
-    # 4. Re-join paragraphs with blank lines
-    return "\n\n".join(humanized)
+def humanize(text):
+    return humanizer.humanize(text)
 
 demo = gr.Interface(
-    fn=humanize_text,
-    inputs=gr.Textbox(lines=20, placeholder="Paste your full SOP here…"),
+    fn=humanize,
+    inputs="textbox",
     outputs="textbox",
-    title="AuthorMist SOP Humanizer",
-    description="Rewrite your entire SOP paragraph-by-paragraph into a natural, undetectable tone."
+    title="AuthorMist AI Humanizer"
 )
 
-if __name__ == "__main__":
-    demo.launch()
+demo.launch()