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("./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()