Update app.py

app.py

@@ -1,32 +1,120 @@
 import streamlit as st
 from transformers import pipeline
+import torch
 
-# Load Emotion classifier (GoEmotions multilingual)
-emotion_model = "joeddav/xlm-roberta-large-xnli"
-emotion_classifier = pipeline("text-classification", model=emotion_model, top_k=None)
+# -------------------------
+# Config / Model names
+# -------------------------
+EMOTION_MODEL = "cardiffnlp/twitter-roberta-base-emotion"
+SENTIMENT_MODEL = "cardiffnlp/twitter-xlm-roberta-base-sentiment"
 
-# Load Tone/Sentiment classifier (same as before)
-tone_model = "cardiffnlp/twitter-xlm-roberta-base-sentiment"
-tone_classifier = pipeline("text-classification", model=tone_model, top_k=None)
+# -------------------------
+# 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"
 
-st.title("🎭 Emotion & Tone Detector (EN / ES / FR)")
+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"
 
-text = st.text_area("✍️ Enter a sentence in English, Spanish, or French:")
+# -------------------------
+# Load pipelines (cached)
+# -------------------------
+@st.cache_resource(show_spinner=False)
+def load_pipelines():
+    device = 0 if torch.cuda.is_available() else -1
 
-if st.button("Analyze"):
-    if text.strip():
-        # Detect emotion
-        emotions = emotion_classifier(text, truncation=True)[0]
-        emotions_sorted = sorted(emotions, key=lambda x: x['score'], reverse=True)
-        top_emotion = emotions_sorted[0]
+    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."
+)
 
-        # Detect tone (sentiment)
-        tones = tone_classifier(text, truncation=True)[0]
-        tones_sorted = sorted(tones, key=lambda x: x['score'], reverse=True)
-        top_tone = tones_sorted[0]
+emotion_pipe, sentiment_pipe = load_pipelines()
 
-        st.write(f"**Emotion:** {top_emotion['label']} (confidence {top_emotion['score']:.2f})")
-        st.write(f"**Tone:** {top_tone['label']} (confidence {top_tone['score']:.2f})")
+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:
-        st.warning("⚠️ Please enter some text.")
+        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)