Create app.py

app.py

@@ -0,0 +1,60 @@
+import gradio as gr
+from transformers import pipeline
+
+# Load models
+english_model = pipeline(
+    "sentiment-analysis",
+    model="cardiffnlp/twitter-roberta-base-sentiment-latest"
+)
+
+urdu_model = pipeline(
+    "sentiment-analysis",
+    model="mrgmd01/sentiment_model_FineTune_cardiffnlp"
+)
+
+# Simple language detection (very lightweight)
+def detect_language(text):
+    urdu_chars = set("ابتثجحخدذرزسشصضطظعغفقکلمنوہیءآؤئۀ")
+    for ch in text:
+        if ch in urdu_chars:
+            return "urdu"
+    return "english"
+
+# Normalize labels into Positive, Negative, Neutral
+def normalize_label(label):
+    label = label.lower()
+    if "positive" in label:
+        return "positive"
+    elif "negative" in label:
+        return "negative"
+    else:
+        return "neutral"
+
+# Main prediction function
+def predict_sentiment(text):
+    if not text.strip():
+        return "Please enter a sentence."
+
+    lang = detect_language(text)
+
+    if lang == "english":
+        result = english_model(text)[0]
+    else:
+        result = urdu_model(text)[0]
+
+    sentiment = normalize_label(result["label"])
+    score = round(result["score"], 3)
+
+    return f"Sentiment: {sentiment} (confidence: {score})"
+
+# Gradio interface
+iface = gr.Interface(
+    fn=predict_sentiment,
+    inputs=gr.Textbox(lines=3, placeholder="Enter a sentence in English, Urdu, or Roman Urdu..."),
+    outputs="text",
+    title="Multilingual Sentiment Analysis",
+    description="This app predicts sentiment (Positive, Negative, Neutral) for English using CardiffNLP and for Urdu/Roman Urdu using a fine-tuned model."
+)
+
+if __name__ == "__main__":
+    iface.launch()