Create app.py

app.py

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+# app.py
+import gradio as gr
+from datasets import load_dataset
+from sentence_transformers import SentenceTransformer
+import faiss
+import numpy as np
+
+# ------------------------------
+# Load FAQ dataset
+# ------------------------------
+dataset = load_dataset("MakTek/Customer_support_faqs_dataset")
+
+faq_questions = [item['question'] for item in dataset['train']]
+faq_answers = [item['answer'] for item in dataset['train']]
+
+# ------------------------------
+# Build embeddings + FAISS index
+# ------------------------------
+embedder = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
+faq_embeddings = embedder.encode(faq_questions, convert_to_numpy=True)
+
+dimension = faq_embeddings.shape[1]
+index = faiss.IndexFlatIP(dimension)
+faiss.normalize_L2(faq_embeddings)
+index.add(faq_embeddings)
+
+# ------------------------------
+# Retrieval function
+# ------------------------------
+def get_answer(query, top_k=1, threshold=0.6):
+    q_emb = embedder.encode([query], convert_to_numpy=True)
+    faiss.normalize_L2(q_emb)
+    
+    distances, indices = index.search(q_emb, top_k)
+    best_score = distances[0][0]
+    best_idx = indices[0][0]
+    
+    if best_score >= threshold:
+        return faq_answers[best_idx], best_score, faq_questions[best_idx]
+    else:
+        return "Sorry, I don’t know the answer to that.", best_score, None
+
+# ------------------------------
+# Gradio response function
+# ------------------------------
+def respond(query, history):
+    if not query.strip():
+        return history, history, ""
+    
+    answer, score, matched_q = get_answer(query)
+    
+    # Optional: remove this line if you want a cleaner bot
+    if matched_q:
+        answer = f"{answer}\n\n(Matched FAQ: \"{matched_q}\" | score={score:.2f})"
+    
+    history = history + [(query, answer)]
+    return history, history, ""
+
+# ------------------------------
+# Gradio UI
+# ------------------------------
+with gr.Blocks() as demo:
+    gr.Markdown("## Customer Support FAQ Chatbot\nAsk me a question about our services.")
+    
+    chatbot = gr.Chatbot(label="Support Bot", height=500)
+    state = gr.State([])
+
+    with gr.Row():
+        with gr.Column(scale=8):
+            txt = gr.Textbox(placeholder="Type your question here...", label=None)
+        with gr.Column(scale=2):
+            send_btn = gr.Button("Send")
+            clear_btn = gr.Button("Clear Chat")
+
+    send_btn.click(respond, inputs=[txt, state], outputs=[chatbot, state, txt])
+    txt.submit(respond, inputs=[txt, state], outputs=[chatbot, state, txt])
+
+    def clear_history():
+        return [], [], ""
+    clear_btn.click(clear_history, inputs=None, outputs=[chatbot, state, txt])
+
+demo.launch()