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| import streamlit as st | |
| from transformers import pipeline | |
| import torch | |
| # ------------------------- | |
| # Config / Model names | |
| # ------------------------- | |
| EMOTION_MODEL = "cardiffnlp/twitter-roberta-base-emotion" | |
| SENTIMENT_MODEL = "cardiffnlp/twitter-xlm-roberta-base-sentiment" | |
| # ------------------------- | |
| # Helpers | |
| # ------------------------- | |
| def map_emotion_to_tts_label(emotion_label: str) -> str: | |
| e = emotion_label.lower() | |
| if e in {"joy", "happiness", "happy", "amusement", "excited", "excitement", "optimism"}: | |
| return "happy / energetic" | |
| if e in {"sadness", "sad", "grief", "disappointed", "disappointment", " melancholy"}: | |
| return "sad / soft / calm" | |
| if e in {"anger", "angry", "annoyance", "annoyed", "disgust"}: | |
| return "angry / intense" | |
| if e in {"fear", "scared", "nervous", "anxious"}: | |
| return "scared / tense" | |
| if e in {"surprise", "surprised"}: | |
| return "surprised / alert" | |
| return "neutral / plain" | |
| def map_sentiment_to_tts_label(sentiment_label: str) -> str: | |
| s = sentiment_label.lower() | |
| if s == "positive": | |
| return "positive / warm" | |
| if s == "negative": | |
| return "negative / firm" | |
| return "neutral / plain" | |
| # ------------------------- | |
| # Load pipelines (cached) | |
| # ------------------------- | |
| def load_pipelines(): | |
| device = 0 if torch.cuda.is_available() else -1 | |
| emotion_pipe = pipeline( | |
| "text-classification", | |
| model=EMOTION_MODEL, | |
| top_k=None, | |
| device=device | |
| ) | |
| sentiment_pipe = pipeline( | |
| "text-classification", | |
| model=SENTIMENT_MODEL, | |
| top_k=None, | |
| device=device | |
| ) | |
| return emotion_pipe, sentiment_pipe | |
| # ------------------------- | |
| # Streamlit UI | |
| # ------------------------- | |
| st.set_page_config(page_title="Emotion + Tone Detector", page_icon="👀", layout="centered") | |
| st.title("👀 Emotion & Tone Detector — English / Spanish / French") | |
| st.write( | |
| "Type a sentence (English / Spanish / French) and click **Analyze**. " | |
| "Shows emotion + tone + suggested TTS style." | |
| ) | |
| emotion_pipe, sentiment_pipe = load_pipelines() | |
| text = st.text_area("✍️ Enter sentence here", height=140, placeholder="Type in English, Spanish, or French...") | |
| if st.button("Analyze"): | |
| if not text or not text.strip(): | |
| st.warning("Please enter a sentence to analyze.") | |
| else: | |
| with st.spinner("Analyzing..."): | |
| try: | |
| # Emotion | |
| emotion_results = emotion_pipe(text, top_k=None) | |
| if isinstance(emotion_results, dict): | |
| emotion_results = [emotion_results] | |
| emotion_results_sorted = sorted(emotion_results, key=lambda x: x["score"], reverse=True) | |
| top_emotion = emotion_results_sorted[0]["label"] | |
| top_emotion_score = emotion_results_sorted[0]["score"] | |
| # Tone/Sentiment | |
| sentiment_results = sentiment_pipe(text, top_k=None) | |
| if isinstance(sentiment_results, dict): | |
| sentiment_results = [sentiment_results] | |
| sentiment_sorted = sorted(sentiment_results, key=lambda x: x["score"], reverse=True) | |
| top_tone = sentiment_sorted[0]["label"] | |
| top_tone_score = sentiment_sorted[0]["score"] | |
| # Map for TTS | |
| tts_from_emotion = map_emotion_to_tts_label(top_emotion) | |
| tts_from_tone = map_sentiment_to_tts_label(top_tone) | |
| # Show results | |
| st.subheader("🎭 Detected Emotion") | |
| st.write(f"**{top_emotion}** (confidence **{top_emotion_score:.2f}**)") | |
| st.subheader("🎵 Detected Tone (Sentiment)") | |
| st.write(f"**{top_tone}** (confidence **{top_tone_score:.2f}**)") | |
| st.subheader("🔊 Suggested TTS style (from emotion)") | |
| st.write(tts_from_emotion) | |
| st.subheader("🔊 Suggested TTS style (from tone)") | |
| st.write(tts_from_tone) | |
| st.subheader("📊 Full emotion scores") | |
| st.table([{ "label": r["label"], "score": f"{r['score']:.3f}"} for r in emotion_results_sorted]) | |
| st.subheader("📊 Full tone (sentiment) scores") | |
| st.table([{ "label": r["label"], "score": f"{r['score']:.3f}"} for r in sentiment_sorted]) | |
| except Exception as err: | |
| st.error("Error during analysis.") | |
| st.exception(err) | |