updated README.md

README.md

@@ -44,5 +44,40 @@ The model was tested on **991** college‑email samples. Below are the per‑cla
 
 ## Usage
 
+### 1. Install dependencies
 ```bash
-pip install tensorflow sentence-transformers
+pip install tensorflow sentence-transformers huggingface_hub
+```
+### 2. Load the model & embedder
+``` python
+from sentence_transformers import SentenceTransformer
+import tensorflow as tf
+from huggingface_hub import hf_hub_download
+
+# 1) Load SBERT embedder
+embedder = SentenceTransformer("all-MiniLM-L6-v2")
+
+# 2) Load your fine‑tuned classifier
+model_file = hf_hub_download(
+    repo_id="skgezhil2005/email_classifier", 
+    filename="model_v2.keras" #replace with your model file 
+)
+model = tf.keras.models.load_model(model_file)
+
+# 3) Define label names (in the same order used during training)
+labels = ["Academics", "Clubs", "Internships", "Others", "Talks"]
+```
+### 3. Inference Helper
+
+``` python
+def classify_email(text: str) -> str:
+    # Compute a 1×384 SBERT embedding
+    emb = embedder.encode(text, convert_to_tensor=False)
+    emb = emb.reshape(1, -1)
+    # Predict probabilities and pick the highest‐scoring class
+    prediction = model.predict(emb)
+    pred_idx = int(np.argmax(prediction[0]))
+
+    return labels[pred_idx]
+
+```