Update app.py

app.py

@@ -2,13 +2,11 @@ import os
 import gradio as gr
 import fitz  # PyMuPDF
 import docx
-import requests
 import numpy as np
-HF_TOKEN = os.getenv("HF_TOKEN")
-API_URL = "https://router.huggingface.co/hf-inference/models/sentence-transformers/all-MiniLM-L6-v2/pipeline/sentence-similarity"
-headers = {
-    "Authorization": f"Bearer {os.environ['HF_TOKEN']}",
-}
+from sentence_transformers import SentenceTransformer
+
+# 1. Load a pretrained Sentence Transformer model locally
+model = SentenceTransformer("all-MiniLM-L6-v2")
 
 
 # ---- Text extraction ----
@@ -25,16 +23,11 @@ def extract_text(file):
     return ""
 
 
-# ---- API embedding helper ----
+# ---- Local embedding helper ----
 def get_embedding(text):
-    payload = {"inputs": text}
-    resp = requests.post(API_URL, headers=headers, json=payload, timeout=60)
-    data = resp.json()
-    if isinstance(data, list) and "embedding" in data[0]:
-        return np.array(data[0]["embedding"])
-    elif isinstance(data, list) and isinstance(data[0], list):
-        return np.array(data[0])
-    return np.zeros(384)
+    # Use the locally loaded model to generate embeddings
+    embedding = model.encode(text)
+    return np.array(embedding)
 
 
 # ---- CV ranking ----
@@ -71,11 +64,9 @@ demo = gr.Interface(
         gr.File(label="📁 Upload CVs (PDF/DOCX)", file_count="multiple", type="binary"),
     ],
     outputs=gr.Markdown(),
-    title="📄 AI CV Ranker (API-powered)",
-    description="Ranks uploaded CVs based on job relevance using Hugging Face API.",
+    title="📄 AI CV Ranker (Local Model)",
+    description="Ranks uploaded CVs based on job relevance using local SentenceTransformer model.",
 )
 
 if __name__ == "__main__":
-    demo.launch()
-
-
+    demo.launch()