app.py

import gradio as gr
from gtts import gTTS
import torch
from transformers import pipeline, AutoModelForSequenceClassification, AutoTokenizer
import tempfile
import nltk
import speech_recognition as sr
from pydub import AudioSegment

# Ensure nltk tokenizer is available
try:
    nltk.data.find('tokenizers/punkt')
except LookupError:
    nltk.download('punkt')

# Load sentiment models
simple_model = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
llm_tokenizer = AutoTokenizer.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
llm_model = AutoModelForSequenceClassification.from_pretrained("cardiffnlp/twitter-roberta-base-sentiment")
llm_pipe = pipeline("sentiment-analysis", model=llm_model, tokenizer=llm_tokenizer)

def transcribe_audio(audio_path):
    recognizer = sr.Recognizer()
    sound = AudioSegment.from_file(audio_path)
    sound.export("temp.wav", format="wav")

    with sr.AudioFile("temp.wav") as source:
        audio = recognizer.record(source)
        try:
            return recognizer.recognize_google(audio)
        except sr.UnknownValueError:
            return ""
        except sr.RequestError:
            return ""

def classify_sentiment(text, model_choice):
    if not text.strip():
        return "🤔 Could not understand audio.", None

    result = (
        simple_model(text)[0] if model_choice == "Simple Model"
        else llm_pipe(text)[0]
    )

    label = result['label']
    mood = {
        "POSITIVE": "😊 Happy / Positive",
        "NEGATIVE": "😞 Sad / Negative",
        "NEUTRAL": "😐 Neutral"
    }.get(label.upper(), label)

    tts = gTTS(text=mood)
    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as tmp:
        tts.save(tmp.name)
        return mood, tmp.name

def predict(audio_file, model_choice):
    if audio_file is None:
        return "No input", None
    text = transcribe_audio(audio_file)
    return classify_sentiment(text, model_choice)

with gr.Blocks() as demo:
    model_choice = gr.Dropdown(["Simple Model", "LLM Model"], value="Simple Model", label="Choose Model")
    gr.Markdown("## 🎙️ Mood from Voice (via Upload)")
    audio_input = gr.Audio(source="upload", type="filepath", label="Upload or Record Your Voice")
    output_text = gr.Textbox(label="Predicted Mood")
    output_audio = gr.Audio(label="Spoken Mood")
    audio_input.change(predict, inputs=[audio_input, model_choice], outputs=[output_text, output_audio])

demo.launch()