Delete app.py

app.py

@@ -1,129 +0,0 @@
-import gradio as gr
-import torch
-from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForSequenceClassification
-from transformers.pipelines import pipeline
-from sentence_transformers import SentenceTransformer, util
-import numpy as np
-import gradio.themes as grthemes
-
-# Paraphrasing model: tuner007/pegasus_paraphrase
-PARAPHRASE_MODEL_NAME = "tuner007/pegasus_paraphrase"
-paraphrase_tokenizer = AutoTokenizer.from_pretrained(PARAPHRASE_MODEL_NAME)
-paraphrase_model = AutoModelForSeq2SeqLM.from_pretrained(PARAPHRASE_MODEL_NAME)
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
-paraphrase_model = paraphrase_model.to(device)
-
-# AI Detector: desklib/ai-text-detector-v1.01
-AI_DETECTOR_MODEL = "desklib/ai-text-detector-v1.01"
-ai_detector = pipeline("text-classification", model=AI_DETECTOR_MODEL, device=0 if torch.cuda.is_available() else -1)
-
-# Semantic similarity model
-similarity_model = SentenceTransformer('all-MiniLM-L6-v2')
-
-def paraphrase(text):
-    prompt = text.strip()
-    batch = paraphrase_tokenizer([prompt], truncation=True, padding='longest', max_length=60, return_tensors="pt").to(device)
-    translated = paraphrase_model.generate(
-        **batch,
-        max_length=60,
-        num_beams=5,
-        num_return_sequences=1,
-        temperature=1.0
-    )
-    tgt_text = paraphrase_tokenizer.batch_decode(translated, skip_special_tokens=True)
-    return tgt_text[0] if tgt_text else ""
-
-def semantic_similarity(text1, text2):
-    emb1 = similarity_model.encode(text1, convert_to_tensor=True)
-    emb2 = similarity_model.encode(text2, convert_to_tensor=True)
-    sim = util.pytorch_cos_sim(emb1, emb2).item()
-    return sim
-
-def ai_detect(text):
-    # Returns probability of being AI-generated (label 'LABEL_1' = AI, 'LABEL_0' = Human)
-    result = ai_detector(text)
-    for r in result:
-        if r['label'] in ['LABEL_1', 'Fake']:
-            return r['score']
-        elif r['label'] in ['LABEL_0', 'Real']:
-            return 1.0 - r['score']
-    return 0.5  # fallback
-
-def humanization_score(sim, ai_prob):
-    # Lower similarity and lower AI probability = more human
-    score = (1.0 - sim) * 0.5 + (1.0 - ai_prob) * 0.5
-    return score
-
-def humanization_rating(score):
-    if score < 0.7:
-        return f"⚠️ Still robotic ({score:.2f})"
-    elif score < 0.85:
-        return f"👍 Acceptable ({score:.2f})"
-    else:
-        return f"✅ Highly Human ({score:.2f})"
-
-def process(text, tone):
-    if not text.strip():
-        return "", "", 0.0, "", 0.0
-    # Pre-humanization AI detection
-    pre_ai_prob = ai_detect(text)
-    # Paraphrase
-    try:
-        paraphrased = paraphrase(text)
-    except Exception as e:
-        return "[Error in paraphrasing: {}]".format(str(e)), "", 0.0, "", 0.0
-    # Post-humanization AI detection
-    post_ai_prob = ai_detect(paraphrased)
-    # Semantic similarity
-    sim = semantic_similarity(text, paraphrased)
-    # Humanization score
-    score = humanization_score(sim, post_ai_prob)
-    rating = humanization_rating(score)
-    ai_score_str = f"Pre: {pre_ai_prob*100:.1f}% | Post: {post_ai_prob*100:.1f}%"
-    return (
-        paraphrased,         # gr.Textbox (string)
-        ai_score_str,        # gr.Markdown (string)
-        sim,                # gr.Number (float)
-        rating,             # gr.Markdown (string)
-        score * 100         # gr.Number (float)
-    )
-
-# Custom dark theme using gradio.themes.Base
-custom_theme = grthemes.Base(
-    primary_hue="blue",
-    secondary_hue="blue",
-    neutral_hue="slate"
-)
-
-with gr.Blocks(theme=custom_theme) as demo:
-    gr.Markdown("""
-    # 🧠 AI Humanizer
-    <div style='display:flex;justify-content:space-between;align-items:center;'>
-        <span style='font-size:1.2em;color:#7bb1ff;'>Rewrite AI text to sound 100% human</span>
-        <span style='font-weight:bold;color:#7bb1ff;'>Made by Taha</span>
-    </div>
-    """, elem_id="header")
-    with gr.Row():
-        with gr.Column():
-            text_in = gr.Textbox(label="Paste AI-generated text here", lines=8, placeholder="Paste your text...")
-            tone = gr.Radio(["Academic", "Casual", "Friendly", "Stealth"], value="Stealth", label="Tone Selector")
-            btn = gr.Button("Humanize", elem_id="humanize-btn")
-        with gr.Column():
-            text_out = gr.Textbox(label="Humanized Output", lines=8, interactive=False)
-            ai_scores = gr.Markdown("", elem_id="ai-scores")
-            sim_score = gr.Number(label="Similarity (0=very different, 1=very similar)", interactive=False)
-            rating = gr.Markdown("", elem_id="rating")
-            human_score = gr.Number(label="Humanization Score (%)", interactive=False)
-    btn.click(
-        process,
-        inputs=[text_in, tone],
-        outputs=[text_out, ai_scores, sim_score, rating, human_score],
-        api_name="humanize"
-    )
-    gr.Markdown("""
-    <div style='text-align:center;color:#7bb1ff;margin-top:2em;'>
-        <b>Made by Taha</b> | Free for unlimited use | Optimized for students
-    </div>
-    """, elem_id="footer")
-
-demo.launch()