Update app.py

app.py

@@ -1,68 +1,115 @@
 import os
 os.environ["HF_HUB_ENABLE_HF_TRANSFER"] = "0"
 
-from transformers import MBart50TokenizerFast, MBartForConditionalGeneration, AutoConfig, pipeline
+from transformers import MBart50TokenizerFast, MBartForConditionalGeneration, AutoConfig
 import gradio as gr
 
-# ---- Load models ----
+# ---- Load model & tokenizer ----
 model_name = "Mudasir692/mbart-eng-ur"
+
+# Fix config issue
 config = AutoConfig.from_pretrained(model_name)
-if config.early_stopping is None:
+if getattr(config, "early_stopping", None) is None:
     config.early_stopping = True
 
 tokenizer = MBart50TokenizerFast.from_pretrained(model_name)
 model = MBartForConditionalGeneration.from_pretrained(model_name, config=config)
 
-# Grammar correction pipeline
-grammar_corrector = pipeline("text2text-generation", model="vennify/t5-base-grammar-correction")
-
 # ---- Language mapping ----
 LANG_CODES = {
     "Urdu": "ur_PK",
     "Arabic": "ar_AR",
     "Hindi": "hi_IN",
     "French": "fr_XX",
+    "German": "de_DE",
     "Spanish": "es_XX",
+    "Chinese": "zh_CN",
+    "Italian": "it_IT",
+    "Portuguese": "pt_XX",
+    "Russian": "ru_RU",
+    "Japanese": "ja_XX",
+    "Korean": "ko_KR",
+    "Turkish": "tr_TR",
+    "Persian": "fa_IR",
+    "Bengali": "bn_IN",
+    "Punjabi": "pa_IN",
+    "Pashto": "ps_AF",
+    "Malay": "ms_MY",
+    "Indonesian": "id_ID",
+    "Tamil": "ta_IN"
 }
 
 # ---- Translation function ----
-def translate_text(text, target_lang, correct_grammar):
+def translate_text(text, target_lang, auto_detect):
     if not text.strip():
-        return "Please enter some English text."
+        return "⚠️ Please enter text to translate."
 
-    # Step 1: Grammar correction (if enabled)
-    corrected_text = text
-    if correct_grammar:
-        result = grammar_corrector(text, max_length=128, num_beams=4)
-        corrected_text = result[0]['generated_text']
+    # Source language
+    if auto_detect:
+        # Very simple heuristic-based detection
+        if any("\u0600" <= ch <= "\u06FF" for ch in text):
+            src_lang = "ur_PK"
+        elif any("\u0900" <= ch <= "\u097F" for ch in text):
+            src_lang = "hi_IN"
+        else:
+            src_lang = "en_XX"
+    else:
+        src_lang = "en_XX"
 
-    # Step 2: Translation
     tgt_lang_code = LANG_CODES.get(target_lang, "ur_PK")
-    tokenizer.src_lang = "en_XX"
+
+    tokenizer.src_lang = src_lang
     tokenizer.tgt_lang = tgt_lang_code
 
-    inputs = tokenizer(corrected_text, return_tensors="pt", padding=True)
-    translated_tokens = model.generate(**inputs)
-    translated_text = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
+    inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True)
+    translated_tokens = model.generate(
+        **inputs,
+        max_length=256,
+        num_beams=5,
+        early_stopping=True
+    )
+    output = tokenizer.decode(translated_tokens[0], skip_special_tokens=True)
+    return output
 
-    return f"✅ Corrected English: {corrected_text}\n\n🌐 Translation ({target_lang}): {translated_text}"
+# ---- Examples ----
+examples = [
+    ["How are you?", "Urdu", False],
+    ["Where are you going?", "Arabic", False],
+    ["This is my new project.", "Hindi", False],
+    ["I love learning new languages.", "French", False],
+    ["Can you help me?", "Spanish", False],
+]
 
 # ---- Gradio Interface ----
-app = gr.Interface(
-    fn=translate_text,
-    inputs=[
-        gr.Textbox(label="Enter English Text", placeholder="Type your English sentence here...", lines=2),
-        gr.Dropdown(["Urdu", "Arabic", "Hindi", "French", "Spanish"], label="Select Target Language", value="Urdu"),
-        gr.Checkbox(label="Correct Grammar Before Translation", value=True)
-    ],
-    outputs=gr.Textbox(label="Output (Corrected + Translated)", lines=4),
-    title="🌍 Smart Multi-Language Translator + Grammar Corrector",
-    description="""
+with gr.Blocks(css="""
+    body {background: linear-gradient(to bottom right, #f7f9fb, #e0f7fa);}
+    .gr-button-primary {background-color: #1e3799 !important; color: white !important;}
+""") as app:
+
+    gr.Markdown("""
     <div style='text-align:center;'>
-        <h3>Auto-correct English grammar before translating into multiple languages.</h3>
-        <p style='color:gray;'>Powered by Transformers & Gradio — built by <b>Khurram Basharat</b>.</p>
+        <h2> Multi-Language Translator (mBART)</h2>
+        <p>Translate between English and 20+ languages using a fine-tuned mBART model.</p>
+        <p style='color:gray;'>Built by <b>Khurram Basharat</b> — powered by Hugging Face & Gradio.</p>
     </div>
-    """,
-)
+    """)
+
+    with gr.Row():
+        with gr.Column(scale=1):
+            text_input = gr.Textbox(label="Enter Text", placeholder="Type your sentence here...", lines=4)
+            target_lang = gr.Dropdown(sorted(LANG_CODES.keys()), label="Select Target Language", value="Urdu")
+            auto_detect = gr.Checkbox(label="Auto-detect Source Language", value=False)
+            translate_btn = gr.Button("Translate")
+
+        with gr.Column(scale=1):
+            result_output = gr.Textbox(label="Translation", lines=4)
+            copy_btn = gr.Button("📋 Copy Translation")
+
+    gr.Examples(examples, inputs=[text_input, target_lang, auto_detect])
+
+    # ---- Actions ----
+    translate_btn.click(translate_text, inputs=[text_input, target_lang, auto_detect], outputs=result_output)
+    #copy_btn.click(None, inputs=result_output, outputs=None, _js="(text) => navigator.clipboard.writeText(text)")
 
-app.launch()
+# ---- Launch app ----
+app.launch(server_name="0.0.0.0", server_port=7860)