Create app.py

app.py

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+import gradio as gr
+from transformers import pipeline
+
+# Load language detection model
+lang_classifier = pipeline("text-classification", model="papluca/xlm-roberta-base-language-detection")
+
+# Load translation model (multi-language to English)
+translator = pipeline("translation", model="facebook/nllb-200-distilled-600M")
+
+# Load hate speech detection model
+offensive_classifier = pipeline("text-classification", model="cardiffnlp/twitter-roberta-base-offensive")
+
+# Mapping from ISO 639-1 to NLLB-200 language codes
+LANGUAGE_CODES = {
+    "en": "eng_Latn", "fr": "fra_Latn", "es": "spa_Latn", "de": "deu_Latn",
+    "bg": "bul_Cyrl", "ru": "rus_Cyrl", "it": "ita_Latn", "zh": "zho_Hans", 
+    "ar": "arb_Arab", "pt": "por_Latn", "nl": "nld_Latn", "hi": "hin_Deva"
+}
+
+def analyze_text(text):
+    if not text.strip():
+        return {"error": "No text provided"}, {"error": "No text provided"}
+
+    # Detect language
+    lang_result = lang_classifier(text)
+    detected_language = lang_result[0]['label']
+    language_confidence = lang_result[0]['score']
+    
+    # Convert detected language to NLLB-200 format
+    detected_language_nllb = LANGUAGE_CODES.get(detected_language, "eng_Latn")
+    
+    # Translate if not English
+    translated_text = text
+    if detected_language_nllb != "eng_Latn":
+        translation_result = translator(text, src_lang=detected_language_nllb, tgt_lang="eng_Latn")
+        translated_text = translation_result[0]['translation_text']
+    
+    # Detect hate speech using the translated text
+    hate_result = offensive_classifier(translated_text)
+
+    language_output = {
+        "language": detected_language,
+        "confidence": language_confidence,
+        "original_text": text,
+        "translated_text": translated_text if detected_language_nllb != "eng_Latn" else "Already in English"
+    }
+    
+    hate_output = {
+        "label": hate_result[0]['label'],
+        "score": hate_result[0]['score']
+    }
+
+    return language_output, hate_output
+
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=analyze_text,
+    inputs=gr.Textbox(label="Enter text"),
+    outputs=[
+        gr.JSON(label="Language Detection & Translation"),
+        gr.JSON(label="Hate Speech Detection")
+    ],
+    title="Detect language, translate, and check for offensive speech",
+    description="Enter text..."
+)
+
+# Launch the Gradio app
+iface.launch(server_name="0.0.0.0", server_port=7860, share=True)