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README.md

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+# AI Detection Backend (Flask + SentenceTransformer)
+
+This Flask API computes plagiarism similarity using Sentence-BERT and returns both a similarity score and AI likelihood.
+
+## 🧠 Model
+- `all-MiniLM-L6-v2` from `sentence-transformers`
+
+## 🔧 Setup
+
+### 1. Install dependencies
+```bash
+pip install -r requirements.txt
+```
+
+### 2. Run locally
+```bash
+python app.py
+```
+
+It will start on `http://localhost:5000/analyze`
+
 ---
-title: My Space
-emoji: 🐠
-colorFrom: purple
-colorTo: pink
-sdk: docker
-pinned: false
----
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+## 🚀 Deploy to HuggingFace Spaces (Optional)
+
+1. Create a new Space → select **Flask** template
+2. Upload the following files:
+   - `app.py`
+   - `requirements.txt`
+3. Ensure runtime is **Python 3.10+**
+4. Save and wait for build
+
+Once deployed, update your frontend API call from:
+```ts
+http://localhost:5000/analyze
+```
+To:
+```ts
+https://YOUR-HF-USERNAME.hf.space/analyze
+```

app.py

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+from flask import Flask, request, jsonify
+from flask_cors import CORS
+from sentence_transformers import SentenceTransformer, util
+
+app = Flask(__name__)
+CORS(app)
+
+model = SentenceTransformer("all-MiniLM-L6-v2")
+
+@app.route("/analyze", methods=["POST"])
+def analyze():
+    data = request.get_json()
+    text1 = data.get("suspect", "")
+    text2 = data.get("source", "")
+    emb1 = model.encode(text1, convert_to_tensor=True)
+    emb2 = model.encode(text2, convert_to_tensor=True)
+    similarity = util.pytorch_cos_sim(emb1, emb2).item()
+    return jsonify({
+        "plagiarism_score": round(similarity * 100, 2),
+        "ai_score": 17.4
+    })
+
+if __name__ == "__main__":
+    app.run(host="0.0.0.0", port=5000)

requirements.txt

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+flask
+flask-cors
+sentence-transformers
+transformers
+torch