Create app.py

app.py

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+import gradio as gr
+from googletrans import Translator
+import nltk
+from nltk.tokenize import word_tokenize
+from nltk.corpus import stopwords
+from nltk.stem import WordNetLemmatizer
+
+# Download necessary NLTK data
+nltk.download('punkt', quiet=True)
+nltk.download('stopwords', quiet=True)
+nltk.download('wordnet', quiet=True)
+
+# Initialize components
+translator = Translator()
+
+def natural_language_understanding(text):
+    tokens = word_tokenize(text.lower())
+    stop_words = set(stopwords.words('english'))
+    lemmatizer = WordNetLemmatizer()
+    processed_tokens = [lemmatizer.lemmatize(token) for token in tokens if token not in stop_words]
+    return " ".join(processed_tokens)
+
+def translate_text(text, target_language):
+    translated = translator.translate(text, dest=target_language)
+    return translated.text
+
+def process_input(input_text, feature, target_language):
+    if not input_text:
+        return "No input provided"
+
+    processed_text = natural_language_understanding(input_text)
+
+    if feature == "Translation":
+        result = translate_text(processed_text, target_language)
+    elif feature == "Transcription":
+        result = processed_text
+    else:
+        result = "Invalid feature selected"
+
+    return result
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=process_input,
+    inputs=[
+        gr.Textbox(label="Input Text"),
+        gr.Radio(["Translation", "Transcription"], label="Feature"),
+        gr.Textbox(label="Target Language (for translation)")
+    ],
+    outputs=gr.Textbox(label="Result"),
+    title="Simple Multi-Faceted Chatbot",
+    description="Enter text, choose a feature, and specify a target language for translation if needed."
+)
+
+# Launch the interface
+iface.launch()