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+import gradio as gr
+import numpy as np
+import plotly.express as px
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+MODEL_NAME = "cardiffnlp/twitter-xlm-roberta-base-sentiment"  
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME).to(device)
+model.eval()
+
+def predict_sentiment(text):
+    inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=128)
+    inputs = {k: v.to(device) for k, v in inputs.items()}
+    
+    with torch.no_grad():
+        outputs = model(**inputs)
+        probs = torch.softmax(outputs.logits, dim=-1)[0].cpu().numpy()
+    
+    pred = np.argmax(probs)
+    label = "✅ Positive" if pred == 1 else "❌ Negative"
+    confidence = f"{probs[pred]:.1%}"
+    
+    fig = px.bar(x=["Negative", "Positive"], y=probs, title=f"Sentiment: {label}")
+    fig.update_yaxes(range=[0, 1])
+    
+    return label, confidence, fig
+
+def chat_response(message, history):
+    if not message.strip():
+        return "", history
+    
+    label, conf, plot = predict_sentiment(message)
+    bot_message = f"**Sentiment:** {label}\n**Confidence:** {conf}"
+    
+    history.append({"role": "user", "content": message})
+    history.append({"role": "assistant", "content": bot_message})
+    
+    return "", history, plot
+
+with gr.Blocks(title="Sentiment Chatbot") as demo:
+    gr.Markdown("# 🗣️ Sentiment Chatbot (EN/AR)")
+    
+    with gr.Row():
+        with gr.Column(scale=3):
+            chatbot = gr.Chatbot(type="messages", height=500)
+            msg_input = gr.Textbox(placeholder="اكتب بالعربية أو الإنجليزية...")
+        with gr.Column(scale=1):
+            sentiment_plot = gr.Plot(label="📊 Confidence")
+    
+    msg_input.submit(chat_response, [msg_input, chatbot], [msg_input, chatbot, sentiment_plot])
+
+demo.launch()