Update app.py

app.py

@@ -1,31 +1,42 @@
 import gradio as gr
-import torch
-from transformers import AutoTokenizer, AutoModelForSequenceClassification
-
-# Load model and tokenizer
-model_name = "distilbert-base-uncased-finetuned-sst-2-english"
-tokenizer = AutoTokenizer.from_pretrained(model_name)
-model = AutoModelForSequenceClassification.from_pretrained(model_name)
-
-# Define prediction function
-def analyze_sentiment(text):
-    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
-    with torch.no_grad():
-        outputs = model(**inputs)
-        probs = torch.nn.functional.softmax(outputs.logits, dim=-1)
-        label = torch.argmax(probs).item()
-        confidence = torch.max(probs).item()
-    sentiment = "Positive 😊" if label == 1 else "Negative 😞"
-    return {"Sentiment": sentiment, "Confidence": round(confidence, 4)}
-
-# Build Gradio app
+from transformers import pipeline
+import pandas as pd
+
+# Load multilingual emotion model
+emotion_model = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base", return_all_scores=True)
+sentiment_model = pipeline("sentiment-analysis", model="distilbert-base-multilingual-cased")
+
+def analyze_text(text):
+    # Sentiment
+    sentiment = sentiment_model(text)[0]
+    sentiment_label = sentiment["label"]
+    sentiment_score = round(sentiment["score"], 4)
+
+    # Emotions
+    emotion_results = emotion_model(text)[0]
+    df = pd.DataFrame(emotion_results)
+    df["score"] = df["score"].round(4)
+
+    # Prepare response
+    emotions_dict = {row["label"]: row["score"] for _, row in df.iterrows()}
+
+    result = {
+        "Sentiment": f"{sentiment_label} 😊" if sentiment_label == "POSITIVE" else f"{sentiment_label} 😞",
+        "Confidence": sentiment_score,
+        "Emotions": emotions_dict
+    }
+
+    return result, gr.BarPlot(value=df, x="label", y="score", title="Emotion Confidence")
+
+# Gradio Interface
 demo = gr.Interface(
-    fn=analyze_sentiment,
-    inputs=gr.Textbox(lines=2, placeholder="Type your text here..."),
-    outputs="json",
-    title="Sentiment Analyzer",
-    description="A fine-tuned DistilBERT model for sentiment classification."
+    fn=analyze_text,
+    inputs=gr.Textbox(label="Enter text in any language"),
+    outputs=[gr.JSON(label="Analysis Result"), gr.BarPlot(label="Emotion Breakdown")],
+    title="K1ng Analyzer V3 🌍",
+    description="Now detects sentiment + emotional tone (joy, anger, sadness, fear, surprise) across multiple languages."
 )
 
 if __name__ == "__main__":
     demo.launch()
+