Update app.py

app.py

@@ -1,56 +1,52 @@
-import gradio as gr
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-from deep_translator import GoogleTranslator
-from langdetect import detect
-import torch
-
-# Load model & tokenizer
-MODEL_NAME = "cardiffnlp/twitter-roberta-base-sentiment"
-tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
-model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
-
-# Sentiment labels
-emotion_labels = {
-    0: "Negative 😕",
-    1: "Neutral 😐",
-    2: "Positive 🙂"
-}
-
-# Translator
-translator = GoogleTranslator(source="auto", target="en")
-
-def analyze_sentiment(user_input):
-    # Detect language
-    detected_language = detect(user_input)
-    
-    # Translate if not English
-    translated_text = translator.translate(user_input) if detected_language != "en" else user_input
-    
-    # Tokenize
-    inputs = tokenizer(translated_text, return_tensors="pt", truncation=True, padding=True, max_length=512)
-
-    with torch.no_grad():
-        outputs = model(**inputs)
-
-    logits = outputs.logits
-    predicted_class = torch.argmax(logits, dim=-1).item()
-    emotion = emotion_labels.get(predicted_class, "Unknown")
-
-    return {
-        "Original Text": user_input,
-        "Translated Text": translated_text,
-        "Detected Language": detected_language,
-        "Predicted Sentiment": emotion
-    }
-
-# Build Gradio UI
-iface = gr.Interface(
-    fn=analyze_sentiment,
-    inputs=gr.Textbox(lines=3, placeholder="Enter text in any language..."),
-    outputs="json",
-    title="🌍 Multilingual Sentiment Analysis",
-    description="Enter text in any language. The system will auto-detect, translate to English, and predict sentiment (Positive/Neutral/Negative)."
-)
-
-if __name__ == "__main__":
-    iface.launch()
+import gradio as gr
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+from deep_translator import GoogleTranslator
+from langdetect import detect
+import torch
+
+# Load model & tokenizer
+MODEL_NAME = "cardiffnlp/twitter-roberta-base-sentiment"
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
+
+# Sentiment labels
+emotion_labels = {
+    0: "Negative 😕",
+    1: "Neutral 😐",
+    2: "Positive 🙂"
+}
+
+# Translator
+translator = GoogleTranslator(source="auto", target="en")
+
+def analyze_sentiment(user_input):
+    # Detect language
+    detected_language = detect(user_input)
+    
+    # Translate if not English
+    translated_text = translator.translate(user_input) if detected_language != "en" else user_input
+    
+    # Tokenize
+    inputs = tokenizer(translated_text, return_tensors="pt", truncation=True, padding=True, max_length=512)
+
+    with torch.no_grad():
+        outputs = model(**inputs)
+
+    logits = outputs.logits
+    predicted_class = torch.argmax(logits, dim=-1).item()
+    emotion = emotion_labels.get(predicted_class, "Unknown")
+
+    # Return only predicted sentiment
+    return emotion
+
+# Build Gradio UI
+iface = gr.Interface(
+    fn=analyze_sentiment,
+    inputs=gr.Textbox(lines=3, placeholder="Enter text in any language..."),
+    outputs=gr.Label(label="Predicted Sentiment"),
+    title="🌍 Multilingual Sentiment Analysis",
+    description="Enter text in any language. The system will auto-detect, translate to English, and predict sentiment (Positive/Neutral/Negative)."
+)
+
+if __name__ == "__main__":
+    iface.launch()