Update app.py

app.py

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+import pandas as pd
+import faiss
+import numpy as np
+from sentence_transformers import SentenceTransformer
+import gradio as gr
+from gtts import gTTS  
+import os
+
+# STEP 1: Load your CSV data
+data = pd.read_csv("/content/data.csv")
+
+# STEP 2: Clean the data
+data = data.dropna(subset=["question", "answer"]).reset_index(drop=True)
+data["question"] = data["question"].astype(str)
+data["answer"] = data["answer"].astype(str)
+
+# STEP 3: Load sentence embedding model
+model = SentenceTransformer("all-MiniLM-L6-v2")
+
+# STEP 4: Generate embeddings and FAISS index
+embeddings = model.encode(data['question'].tolist())
+index = faiss.IndexFlatL2(embeddings.shape[1])
+index.add(np.array(embeddings))
+
+# ✅ STEP 5: Define the function with TTS
+def answer_with_audio(user_input):
+    query_embedding = model.encode([user_input])
+    _, I = index.search(np.array(query_embedding), k=1)
+    answer = data.iloc[I[0][0]]['answer']
+
+    # Convert answer to speech using gTTS
+    tts = gTTS(answer)
+    tts_path = "/tmp/tts_output.mp3"
+    tts.save(tts_path)
+
+    return answer, tts_path
+
+# ✅ STEP 6: Create Gradio interface with text + audio output
+gr.Interface(
+    fn=answer_with_audio,
+    inputs=gr.Textbox(lines=2, placeholder="Ask your question..."),
+    outputs=["text", "audio"],
+    title="🔊 Cooking FAQ ",
+    description="Ask your question and listen to the response.",
+).launch()