Update app.py

app.py

@@ -1,60 +1,57 @@
 import gradio as gr
 from transformers import pipeline
-import json
+from langdetect import detect
 import matplotlib.pyplot as plt
 
-# Load models
-sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
-emotion_analyzer = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
-language_detector = pipeline("text-classification", model="papluca/xlm-roberta-base-language-detection")
+# Load multilingual sentiment model
+sentiment_model = pipeline("sentiment-analysis", model="nlptown/bert-base-multilingual-uncased-sentiment")
 
-def analyze_text(text):
-    # Detect language
-    lang_result = language_detector(text)[0]
-    detected_lang = lang_result['label']
-
-    # Sentiment
-    sentiment = sentiment_analyzer(text)[0]
-    label = "Positive 😊" if sentiment["label"] == "POSITIVE" else "Negative 😞"
-    confidence = round(sentiment["score"], 4)
-
-    # Emotions
-    emotion_scores = emotion_analyzer(text)[0]
-    emotions = {e['label']: round(e['score'], 4) for e in emotion_scores}
-    dominant = max(emotions, key=emotions.get)
+# Emotion model (English-based)
+emotion_model = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
 
-    # Plot emotions
+def analyze_text(text):
+    try:
+        # Detect language
+        lang = detect(text)
+    except:
+        lang = "unknown"
+
+    # Sentiment analysis
+    sentiment_result = sentiment_model(text)[0]
+    sentiment_label = sentiment_result['label']
+    confidence = round(sentiment_result['score'], 4)
+
+    # Emotion analysis
+    emotions = emotion_model(text)[0]
+    emotion_scores = {e["label"]: round(e["score"], 4) for e in emotions}
+
+    # Plot emotion chart
     plt.figure(figsize=(6, 3))
-    plt.bar(emotions.keys(), emotions.values(), color='skyblue')
-    plt.title(f"Emotion Breakdown (Dominant: {dominant})")
-    plt.ylabel("Confidence")
-    plt.xticks(rotation=30)
+    plt.bar(emotion_scores.keys(), emotion_scores.values(), color="skyblue")
+    plt.title("Emotion Confidence Levels")
+    plt.ylabel("Score")
+    plt.xticks(rotation=45)
     plt.tight_layout()
     plt.savefig("emotion_chart.png")
     plt.close()
 
-    # JSON output
-    result = {
-        "Detected Language": detected_lang,
-        "Sentiment": label,
+    return {
+        "Detected Language": lang,
+        "Sentiment": sentiment_label,
         "Confidence": confidence,
-        "Emotions": emotions
-    }
-
-    return json.dumps(result, indent=2), "emotion_chart.png"
+        "Emotions": emotion_scores
+    }, "emotion_chart.png"
 
-# Gradio UI
-iface = gr.Interface(
+# Gradio interface
+demo = gr.Interface(
     fn=analyze_text,
-    inputs=gr.Textbox(lines=3, placeholder="Type your text here..."),
+    inputs=gr.Textbox(label="Enter text to analyze"),
     outputs=[
-        gr.JSON(label="Analysis Results"),
-        gr.Image(label="Emotion Visualization")
+        gr.JSON(label="Analysis Result"),
+        gr.Image(label="Emotion Confidence Chart")
     ],
-    title="K1ng Analyzer V3 👑",
-    description="Multilingual Sentiment + Emotion + Language Analyzer with Visualization",
-    theme="gradio/soft"
+    title="K1ng Analyzer V3 🌍🧠",
+    description="Multilingual Sentiment + Emotion Analyzer with Visualization"
 )
 
-iface.launch()
-
+demo.launch()