Update app.py

app.py

@@ -17,7 +17,7 @@ urdu_model = pipeline(
 
 roman_urdu_model = pipeline(
     "sentiment-analysis",
-    model="mrgmd01/sentiment_model_FineTune_cardiffnlp"  # Replace with your Roman Urdu model if available
+    model="mrgmd01/sentiment_model_FineTune_cardiffnlp"  # replace with roman urdu model if available
 )
 
 # File to store only sentences
@@ -48,16 +48,22 @@ def normalize_label(label):
     else:
         return "Neutral"
 
-# Prediction function
+# Add emojis for polarity
+def add_emoji(label):
+    mapping = {
+        "Positive": "😊👍",
+        "Negative": "😞👎",
+        "Neutral": "😐"
+    }
+    return f"{label} {mapping.get(label, '')}"
+
+# Prediction + Save sentence
 def analyze_single(text, lang_hint):
     if not text.strip():
         return "Please enter a sentence.", "", "", SAVE_FILE
 
-    # If user gives hint, use it; else auto-detect
-    if lang_hint and lang_hint != "Auto":
-        lang = lang_hint
-    else:
-        lang = detect_language(text)
+    # Auto detect if user keeps default "English"
+    lang = detect_language(text) if lang_hint == "English" else lang_hint
 
     if lang == "English":
         result = english_model(text)[0]
@@ -67,34 +73,36 @@ def analyze_single(text, lang_hint):
         result = roman_urdu_model(text)[0]
 
     sentiment = normalize_label(result["label"])
+    sentiment_with_emoji = add_emoji(sentiment)
     score = round(result["score"], 3)
-    polarity = "Positive" if sentiment == "Positive" else ("Negative" if sentiment == "Negative" else "Neutral")
 
-    # Save only sentence
+    # Save only the sentence
     df = pd.read_csv(SAVE_FILE)
     new_row = pd.DataFrame([[text]], columns=["Sentence"])
     df = pd.concat([df, new_row], ignore_index=True)
     df.to_csv(SAVE_FILE, index=False)
 
-    return sentiment, str(score), polarity, SAVE_FILE
-
+    return sentiment, str(score), sentiment_with_emoji, SAVE_FILE
 
 # Gradio UI
 with gr.Blocks() as demo:
-    gr.Markdown("## 🌍 Multilingual Sentiment Analysis (Positive • Neutral • Negative)")
-    gr.Markdown("**Languages:** English, Urdu, Roman Urdu  \n"
-                "Model: `cardiffnlp/twitter-roberta-base-sentiment-latest (English)`  \n"
-                "`mrgmd01/sentiment_model_FineTune_cardiffnlp (Urdu & Roman Urdu)`")
-
-    with gr.Tab("Sentiment Analysis"):
+    gr.Markdown(
+        "## 🌍 Multilingual Sentiment Analysis (Positive • Neutral • Negative)\n"
+        "**Languages:** English, Urdu, Roman Urdu  \n"
+        "📌 Models:  \n"
+        "- `cardiffnlp/twitter-roberta-base-sentiment-latest (English)`  \n"
+        "- `mrgmd01/sentiment_model_FineTune_cardiffnlp (Urdu & Roman Urdu)`"
+    )
+
+    with gr.Tab("Sentiment Analyzer"):
         user_text = gr.Textbox(label="Enter text", placeholder="Type in English, Urdu, or Roman Urdu...")
-        lang_dropdown = gr.Dropdown(["Auto", "English", "Urdu", "Roman Urdu"], label="Language Hint", value="Auto")
-        btn = gr.Button("Analyze")
+        lang_dropdown = gr.Dropdown(["English", "Urdu", "Roman Urdu"], label="Language Hint", value="English")
+        btn = gr.Button("🔍 Analyze")
 
         out_sent = gr.Textbox(label="Sentiment")
         out_conf = gr.Textbox(label="Confidence (0–1)")
-        out_pol  = gr.Textbox(label="Polarity")
-        out_file = gr.File(label="Download logs (.csv)", type="filepath")
+        out_pol  = gr.Textbox(label="Polarity + Emoji")
+        out_file = gr.File(label="⬇️ Download Sentences (.csv)", type="filepath")
 
         btn.click(analyze_single, inputs=[user_text, lang_dropdown],
                   outputs=[out_sent, out_conf, out_pol, out_file])