Update app.py

app.py

@@ -3,57 +3,52 @@ from transformers import pipeline
 from indic_transliteration import sanscript
 from indic_transliteration.sanscript import transliterate
 
-# Load Hugging Face models (public & documented)
+#  Emotion Model
 emotion_model = pipeline(
     "text-classification",
     model="vashuag/HindiEmotion",
-    top_k=None
+    return_all_scores=True
 )
 
+#  Sentiment Model
 sentiment_model = pipeline(
     "text-classification",
-    model="madhav112/hindi-sentiment-analysis",
-    top_k=None
+    model="LondonStory/txlm-roberta-hindi-sentiment",
+    return_all_scores=True
 )
 
-# Transliteration: Hinglish -> Hindi
+# Transliterate Hinglish → Hindi
 def hinglish_to_hindi(text):
     return transliterate(text, sanscript.ITRANS, sanscript.DEVANAGARI)
 
-# Core pipeline
+# Full Pipeline
 def analyze_text(hinglish_text):
-    # Step 1: Convert Hinglish -> Raw Hindi
     raw_hindi = hinglish_to_hindi(hinglish_text)
+    clean_hindi = raw_hindi  # Can insert normalization if needed
 
-    # Step 2: Clean Hindi (here = same, but could add preprocessing later)
-    clean_hindi = raw_hindi  
+    emotions = emotion_model(clean_hindi)[0]
+    sentiments = sentiment_model(clean_hindi)[0]
 
-    # Step 3: Run models
-    emotion_scores = emotion_model(clean_hindi)[0]
-    sentiment_scores = sentiment_model(clean_hindi)[0]
-
-    # Format nicely
     return {
         "Hinglish Input": hinglish_text,
         "Raw Hindi": raw_hindi,
-        "Clean Hindi": clean_hindi,
-        "Emotion Scores": {e['label']: round(e['score'], 3) for e in emotion_scores},
-        "Sentiment Scores": {s['label']: round(s['score'], 3) for s in sentiment_scores}
+        "Emotion Scores": {e["label"]: round(e["score"], 3) for e in emotions},
+        "Sentiment Scores": {s["label"]: round(s["score"], 3) for s in sentiments}
     }
 
-# Gradio UI
-demo = gr.Interface(
+iface = gr.Interface(
     fn=analyze_text,
     inputs=gr.Textbox(label="Enter Hinglish Text"),
     outputs="json",
-    title="Hinglish → Hindi Emotion + Sentiment Analysis",
+    title="Hinglish → Hindi Emotion & Sentiment Analyzer",
     description=(
-        "This app converts Hinglish text into Hindi and analyzes it using two **trusted models**:\n"
-        "- vashuag/HindiEmotion (emotions)\n"
-        "- madhav112/hindi-sentiment-analysis (sentiment polarity)\n\n"
-        "Both models are public, documented, and trained on reliable datasets."
+        "This app uses **documented and public models**:\n"
+        "- **vashuag/HindiEmotion** for emotion classification\n"
+        "- **LondonStory/txlm-roberta-hindi-sentiment** for sentiment (3-class)\n"
+        "Both are transparent, reliable, and well-evaluated on Hindi data."
     )
 )
 
 if __name__ == "__main__":
-    demo.launch()
+    iface.launch()
+