Create app.py

app.py

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+import gradio as gr
+import pandas as pd
+from sentence_transformers import SentenceTransformer, util
+
+# ---------- Load data & model (all CPU-friendly) ----------
+faq_df = pd.read_csv("faqs.csv")
+questions = faq_df["question"].tolist()
+answers   = faq_df["answer"].tolist()
+
+model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+question_embeddings = model.encode(questions, convert_to_tensor=True, normalize_embeddings=True)
+
+# ---------- Search function ----------
+def semantic_search(user_query, top_k=3):
+    query_embedding = model.encode(user_query, convert_to_tensor=True, normalize_embeddings=True)
+    scores = util.cos_sim(query_embedding, question_embeddings)[0]
+    top_k_idx = scores.topk(k=top_k).indices.cpu().numpy()
+    
+    results = []
+    for idx in top_k_idx:
+        results.append(
+            {
+                "FAQ Question": questions[idx],
+                "FAQ Answer"  : answers[idx],
+                "Similarity"  : f"{scores[idx]:.3f}"
+            }
+        )
+    return results
+
+# ---------- Gradio UI ----------
+with gr.Blocks(title="MiniLM Semantic FAQ Search") as demo:
+    gr.Markdown(
+        """
+        # 🔍 Semantic FAQ Search  
+        Enter a salon-related question. The model finds the closest FAQs and displays their answers.
+        """)
+    
+    with gr.Row():
+        query_box = gr.Textbox(
+            label="Ask a question",
+            placeholder="e.g. Which spray protects hair from heat?"
+        )
+        topk_slider = gr.Slider(
+            1, 5, value=3, step=1, label="Number of results"
+        )
+    search_btn = gr.Button("Search")
+    out = gr.Dataframe(headers=["FAQ Question", "FAQ Answer", "Similarity"], visible=True, wrap=True)
+    
+    search_btn.click(semantic_search, [query_box, topk_slider], out)
+
+if __name__ == "__main__":
+    demo.launch(server_name="0.0.0.0", show_error=True)