app.py

import pandas as pd
import faiss
import gradio as gr
from datasets import load_dataset
from sentence_transformers import SentenceTransformer
from gtts import gTTS
import tempfile

# Load dataset
dataset = load_dataset("RomainPct/steve-jobs-question-and-answers", split="train")
df = pd.DataFrame(dataset)

# Load embedding model
model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")

# Encode questions
question_embeddings = model.encode(df['instruction'].tolist(), convert_to_numpy=True)

# Build FAISS index
dimension = question_embeddings.shape[1]
index = faiss.IndexFlatL2(dimension)
index.add(question_embeddings)

# Define search function with audio output
def search_faq(user_query, k=1):
    query_embedding = model.encode([user_query], convert_to_numpy=True)
    distances, indices = index.search(query_embedding, k)
    best_idx = indices[0][0]
    answer = df.iloc[best_idx]['output']
    
    # Convert answer to speech and save to temp file
    tts = gTTS(text=answer)
    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as fp:
        tts.save(fp.name)
        return fp.name

# Gradio UI
gr.Interface(
    fn=search_faq,
    inputs=gr.Textbox(label="Ask about Steve Jobs", placeholder="e.g., What did Steve say about innovation?"),
    outputs=gr.Audio(type="filepath"),
    title="Steve Jobs FAQ Assistant (Audio)",
    description="Ask questions based on Steve Jobs interviews. Response is read aloud.",
    theme="soft"
).launch()