Update app.py

app.py

@@ -1,6 +1,5 @@
 import gradio as gr
 from transformers import pipeline, AutoTokenizer, AutoModelForSequenceClassification
-from langdetect import detect
 import pandas as pd
 import os
 
@@ -27,29 +26,26 @@ if not os.path.exists(csv_file):
 # -----------------------------
 # Processing Function
 # -----------------------------
-def analyze_sentiment(sentence):
+def analyze_sentiment(sentence, language):
     try:
-        # Detect language
-        lang = detect(sentence)
-
-        if lang == "en":
+        if language == "English":
             result = en_model(sentence)[0]
         else:
-            # Treat anything non-English as Urdu/Roman Urdu
+            # Urdu or Roman Urdu
             result = ur_pipeline(sentence)[0]
 
         label = result["label"]
         score = round(result["score"], 3)
 
         # Save to CSV
-        new_row = pd.DataFrame([[sentence, lang, f"{label} ({score})"]],
+        new_row = pd.DataFrame([[sentence, language, f"{label} ({score})"]],
                                columns=["Sentence", "Language", "Sentiment"])
         df = pd.read_csv(csv_file)
         df = pd.concat([df, new_row], ignore_index=True)
         df.to_csv(csv_file, index=False)
 
         # Output
-        return f"**Detected Language:** {lang}\n**Sentiment:** {label}\n**Confidence:** {score}"
+        return f"**Language Selected:** {language}\n**Sentiment:** {label}\n**Confidence:** {score}"
 
     except Exception as e:
         return f"⚠️ Error: {str(e)}"
@@ -59,12 +55,13 @@ def analyze_sentiment(sentence):
 # -----------------------------
 with gr.Blocks() as demo:
     gr.Markdown("## 🌍 Multilingual Sentiment Analysis (English, Urdu, Roman Urdu)")
-    gr.Markdown("Enter a sentence in **English, Urdu, or Roman Urdu** to detect sentiment.")
+    gr.Markdown("Enter a sentence and select the language to detect sentiment.")
 
     input_text = gr.Textbox(label="Enter your sentence:")
+    lang_dropdown = gr.Dropdown(choices=["English", "Urdu", "Roman Urdu"], label="Select Language")
     output_text = gr.Markdown(label="Result")
 
     btn = gr.Button("Analyze Sentiment")
-    btn.click(analyze_sentiment, inputs=input_text, outputs=output_text)
+    btn.click(analyze_sentiment, inputs=[input_text, lang_dropdown], outputs=output_text)
 
 demo.launch()