running faiss script

app.py

@@ -2,6 +2,8 @@ import gradio as gr
 from huggingface_hub import InferenceClient
 from datasets import load_dataset
 import time
+import faiss
+import numpy as np
 
 def log(message):
     print(f"✅ {message}")
@@ -88,6 +90,33 @@ demo = gr.Interface(
 )
 
 
+# ✅ Function to check FAISS index
+def check_faiss():
+    index_path = "my_embeddings"  # Adjust if needed
+
+    try:
+        index = faiss.read_index(index_path)
+        num_vectors = index.ntotal
+        dim = index.d
+
+        if num_vectors > 0:
+            sample_vectors = index.reconstruct_n(0, min(5, num_vectors))  # Get first 5 embeddings
+            return f"📊 FAISS index contains {num_vectors} vectors.\n✅ Embedding dimension: {dim}\n🧐 Sample: {sample_vectors[:2]} ..."
+        else:
+            return "⚠️ No embeddings found in FAISS index!"
+
+    except Exception as e:
+        return f"❌ ERROR: Failed to load FAISS index - {e}"
+
+# ✅ Add a Gradio button to trigger FAISS check
+with gr.Blocks() as demo:
+    gr.Markdown("### 🔍 FAISS Embedding Check")
+    
+    check_button = gr.Button("🔎 Check FAISS Embeddings")
+    output_text = gr.Textbox(label="FAISS Status", interactive=False)
+
+    check_button.click(fn=check_faiss, outputs=output_text)
+
 # Launch Gradio app
 if __name__ == "__main__":
     demo.launch()