Update app.py

app.py

@@ -1,5 +1,6 @@
 import gradio as gr
-from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
+from transformers import pipeline
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
 from deep_translator import GoogleTranslator
 
 # Load models
@@ -7,51 +8,54 @@ humanizer = pipeline("text2text-generation", model="humarin/chatgpt_paraphraser_
 detector_model = AutoModelForSequenceClassification.from_pretrained("roberta-base-openai-detector")
 detector_tokenizer = AutoTokenizer.from_pretrained("roberta-base-openai-detector")
 
-# Inference Functions
-def rewrite_text(input_text, tone):
+# Core logic
+def process_text(input_text, tone, lang):
+    if not input_text.strip():
+        return "", "❌ No input text provided", ""
+
+    # Tone-based prefix
     prefix_map = {
-        "Academic": "paraphrase this academically: ",
-        "Professional": "rephrase this professionally: ",
-        "Conversational": "make this sound casual: "
+        "Academic": "Paraphrase this academically: ",
+        "Professional": "Rephrase this professionally: ",
+        "Conversational": "Make this sound casual: "
     }
     prefix = prefix_map.get(tone, "")
-    response = humanizer(prefix + input_text, max_length=512, do_sample=False)[0]['generated_text']
-    return response
+    prompt = prefix + input_text.strip()
+
+    # Humanize
+    humanized = humanizer(prompt, max_length=512, do_sample=False)[0]['generated_text']
 
-def detect_ai_content(text):
-    inputs = detector_tokenizer(text, return_tensors="pt", truncation=True, max_length=512)
+    # Detect AI likelihood
+    inputs = detector_tokenizer(humanized, return_tensors="pt", truncation=True, max_length=512)
     outputs = detector_model(**inputs)
-    score = float(outputs.logits.softmax(dim=1)[0][1])  # AI-likeness score
+    score = float(outputs.logits.softmax(dim=1)[0][1])
     if score > 0.75:
-        verdict = "⚠️ This seems AI-generated (score: {:.2f})".format(score)
+        verdict = f"⚠️ This seems AI-generated (score: {score:.2f})"
     elif score > 0.5:
-        verdict = "❓ Possibly AI-written (score: {:.2f})".format(score)
+        verdict = f"❓ Possibly AI-written (score: {score:.2f})"
+    else:
+        verdict = f"✅ Human-like writing (score: {score:.2f})"
+
+    # Translate
+    if lang == "Bahasa Malaysia":
+        translated = GoogleTranslator(source='auto', target='ms').translate(humanized)
+    elif lang == "Chinese":
+        translated = GoogleTranslator(source='auto', target='zh-CN').translate(humanized)
     else:
-        verdict = "✅ Human-like writing (score: {:.2f})".format(score)
-    return verdict
+        translated = "❎ Translation skipped (None selected)"
 
-def translate_text(text, target_lang):
-    try:
-        if target_lang == "Bahasa Malaysia":
-            translated = GoogleTranslator(source='auto', target='ms').translate(text)
-        elif target_lang == "Chinese":
-            translated = GoogleTranslator(source='auto', target='zh-CN').translate(text)
-        else:
-            translated = GoogleTranslator(source='auto', target='en').translate(text)
-        return translated
-    except Exception as e:
-        return f"Translation error: {str(e)}"
+    return humanized, verdict, translated
 
 # Gradio UI
 with gr.Blocks(theme=gr.themes.Soft()) as demo:
     gr.Markdown("### 🕌 Zawiyah AI Collective – Humanizer AI")
-    gr.Markdown("Transforms text into Academic, Professional, or Conversational style, detects AI content, and translates to Bahasa Malaysia or Chinese.")
+    gr.Markdown("Transforms input into refined **Academic**, **Professional**, or **Conversational** tone. Also detects AI likelihood and optionally translates.")
 
     with gr.Row():
         with gr.Column():
             input_text = gr.Textbox(label="📝 Enter your text", lines=8, placeholder="Paste your content here...")
             tone = gr.Radio(["Academic", "Professional", "Conversational"], label="Select Tone", value="Academic")
-            lang = gr.Radio(["Bahasa Malaysia", "Chinese"], label="Translate To", value="Bahasa Malaysia")
+            lang = gr.Radio(["None", "Bahasa Malaysia", "Chinese"], label="Translate To", value="None")
             submit_btn = gr.Button("🚀 Submit")
             clear_btn = gr.Button("🧹 Clear")
         with gr.Column():
@@ -59,16 +63,11 @@ with gr.Blocks(theme=gr.themes.Soft()) as demo:
             detection = gr.Textbox(label="🤖 AI Detection Result")
             translated = gr.Textbox(label="🌐 Translated Version")
 
-    # Button click events
-    submit_btn.click(fn=rewrite_text, inputs=[input_text, tone], outputs=rewritten)
-    submit_btn.click(fn=detect_ai_content, inputs=input_text, outputs=detection)
-    submit_btn.click(fn=translate_text, inputs=[input_text, lang], outputs=translated)
-    clear_btn.click(lambda: ("", "", "", ""), outputs=[input_text, rewritten, detection, translated])
+    submit_btn.click(fn=process_text, inputs=[input_text, tone, lang], outputs=[rewritten, detection, translated])
+    clear_btn.click(lambda: ("", "", ""), outputs=[rewritten, detection, translated])
 
     gr.Markdown("---")
-    gr.Markdown(
-        "☕ [Buy Me a Coffee](https://www.buymeacoffee.com/humairaai) to support future updates! | Built with ❤️ using [Gradio](https://gradio.app)"
-    )
+    gr.Markdown("☕ [Buy Me a Coffee](https://www.buymeacoffee.com/humairaai) to support future updates | Built with ❤️ using [Gradio](https://gradio.app)")
 
 # Launch
 demo.launch()