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app.py

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+import gradio as gr
+import torch
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+from sentence_transformers import SentenceTransformer, util
+import numpy as np
+import requests
+import gradio.themes as grthemes
+import config
+
+# ----------------------
+# Paraphrasing Model Setup
+# ----------------------
+PARAPHRASE_MODEL_NAME = "Vamsi/T5_Paraphrase_Paws"
+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)
+
+# ----------------------
+# Semantic Similarity Model
+# ----------------------
+similarity_model = SentenceTransformer('all-MiniLM-L6-v2')
+
+# ----------------------
+# Tone Templates
+# ----------------------
+tone_templates = {
+    "Academic": "Paraphrase the following text in a formal, academic tone:",
+    "Casual": "Paraphrase the following text in a casual, conversational tone:",
+    "Friendly": "Paraphrase the following text in a friendly, approachable tone:",
+    "Stealth": "Paraphrase the following text to bypass AI detectors and sound as human as possible:",
+}
+
+# ----------------------
+# Paraphrasing Function
+# ----------------------
+def paraphrase(text, tone):
+    prompt = tone_templates[tone] + " " + text
+    input_ids = paraphrase_tokenizer.encode(prompt, return_tensors="pt", max_length=256, truncation=True).to(device)
+    outputs = paraphrase_model.generate(
+        input_ids,
+        do_sample=True,
+        top_k=120,
+        top_p=0.95,
+        temperature=0.7,
+        repetition_penalty=1.2,
+        max_length=256,
+        num_return_sequences=1
+    )
+    paraphrased = paraphrase_tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return paraphrased
+
+# ----------------------
+# Semantic Similarity Function
+# ----------------------
+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
+
+# ----------------------
+# Real AI Detection (Winston AI API)
+# ----------------------
+def check_ai_score(text):
+    api_key = config.WINSTON_AI_API_KEY
+    api_url = config.WINSTON_AI_API_URL
+    if not api_key:
+        return None, "No API key set. Please add your Winston AI API key to config.py."
+    headers = {
+        "Authorization": f"Bearer {api_key}",
+        "Content-Type": "application/json"
+    }
+    data = {"text": text, "sentences": False}
+    try:
+        response = requests.post(api_url, headers=headers, json=data, timeout=30)
+        if response.status_code == 200:
+            result = response.json()
+            # Winston AI returns a 'score' (0-100, higher = more human)
+            score = result.get("score", None)
+            if score is not None:
+                ai_prob = 1.0 - (score / 100.0)
+                return ai_prob, None
+            else:
+                return None, "No score in Winston AI response."
+        else:
+            return None, f"Winston AI error: {response.status_code} {response.text}"
+    except Exception as e:
+        return None, f"Winston AI exception: {str(e)}"
+
+# ----------------------
+# Humanization Score & Rating
+# ----------------------
+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 AI-like ({score:.2f})"
+    elif score < 0.85:
+        return f"👍 Acceptable ({score:.2f})"
+    else:
+        return f"✅ Highly Humanized ({score:.2f})"
+
+# ----------------------
+# Main Processing Function
+# ----------------------
+def process(text, tone):
+    if not text.strip():
+        return "", "", 0.0, "", 0.0, ""
+    # Pre-humanization AI detection
+    pre_ai_prob, pre_err = check_ai_score(text)
+    if pre_ai_prob is None:
+        return "", f"AI Detection Error: {pre_err}", 0.0, "", 0.0, ""
+    # Paraphrase
+    try:
+        paraphrased = paraphrase(text, tone)
+    except Exception as e:
+        return f"[Paraphrasing error: {str(e)}]", "", 0.0, "", 0.0, ""
+    # Post-humanization AI detection
+    post_ai_prob, post_err = check_ai_score(paraphrased)
+    if post_ai_prob is None:
+        return paraphrased, f"AI Detection Error: {post_err}", 0.0, "", 0.0, ""
+    # 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: {100*(1-pre_ai_prob):.1f}% human | Post: {100*(1-post_ai_prob):.1f}% human"
+    return (
+        paraphrased,         # gr.Textbox (string)
+        ai_score_str,        # gr.Markdown (string)
+        sim,                # gr.Number (float)
+        rating,             # gr.Markdown (string)
+        score * 100,        # gr.Number (float)
+        ""
+    )
+
+# ----------------------
+# Gradio UI
+# ----------------------
+custom_theme = grthemes.Base(
+    primary_hue="blue",
+    secondary_hue="blue",
+    neutral_hue="slate"
+)
+
+with gr.Blocks(theme=custom_theme, title="AI Humanizer - Made by Taha") 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...", elem_id="input-box")
+            tone = gr.Dropdown(["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, elem_id="output-box")
+            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, gr.Textbox(visible=False)],
+        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 and creators
+    </div>
+    """, elem_id="footer")
+
+demo.launch() 

config.py

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+# config.py
+# Place your API keys and endpoints here for AI detection services
+
+WINSTON_AI_API_KEY = "5lC2zMKk3cDVUhk2augozEK9jZYEUiGYfcqKIEC2bee7261a"  # Add your Winston AI API key here
+WINSTON_AI_API_URL = "https://api.gowinston.ai/v2/ai-content-detection"  # Official v2 endpoint for text detection
+
+SAPLING_API_KEY = ""
+SAPLING_API_URL = "https://api.sapling.ai/api/v1/aidetect"  # Example, update as needed
+
+# Add more as needed 

requirements.txt

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+gradio>=3.50.2
+transformers>=4.40.0
+torch>=2.0.0
+sentence-transformers>=2.6.1
+sentencepiece>=0.1.99
+requests>=2.31.0
+scikit-learn>=1.0.2
+numpy>=1.21.0
+# For Hugging Face Spaces and API integration