Update app.py

app.py

@@ -1,9 +1,14 @@
-import gradio as gr
+import speech_recognition as sr
 from googletrans import Translator
+from textblob import TextBlob
 import nltk
 from nltk.tokenize import word_tokenize
 from nltk.corpus import stopwords
 from nltk.stem import WordNetLemmatizer
+from gtts import gTTS
+import gradio as gr
+import tempfile
+import os
 
 # Download necessary NLTK data
 nltk.download('punkt', quiet=True)
@@ -11,6 +16,7 @@ nltk.download('stopwords', quiet=True)
 nltk.download('wordnet', quiet=True)
 
 # Initialize components
+recognizer = sr.Recognizer()
 translator = Translator()
 
 def natural_language_understanding(text):
@@ -24,33 +30,80 @@ def translate_text(text, target_language):
     translated = translator.translate(text, dest=target_language)
     return translated.text
 
-def process_input(input_text, feature, target_language):
+def text_to_speech(text):
+    tts = gTTS(text)
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as fp:
+        tts.save(fp.name)
+    return fp.name
+
+def process_input(input_text, input_audio, feature, target_language, output_language):
+    if input_audio is not None:
+        # Process audio input
+        try:
+            with sr.AudioFile(input_audio) as source:
+                audio = recognizer.record(source)
+            input_text = recognizer.recognize_google(audio)
+        except sr.UnknownValueError:
+            return "Could not understand audio", None
+        except sr.RequestError:
+            return "Could not request results from speech recognition service", None
+        except Exception as e:
+            return f"An error occurred: {str(e)}", None
+
     if not input_text:
-        return "No input provided"
+        return "No input provided", None
 
     processed_text = natural_language_understanding(input_text)
 
     if feature == "Translation":
         result = translate_text(processed_text, target_language)
+    elif feature == "Voice Command":
+        result = "Voice command feature not implemented in this example"
     elif feature == "Transcription":
         result = processed_text
     else:
         result = "Invalid feature selected"
 
-    return result
+    if output_language:
+        result = translate_text(result, output_language)
+
+    return result, None
+
+def tts_function(text):
+    if text:
+        return text_to_speech(text)
+    return None
 
 # 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."
-)
+with gr.Blocks() as iface:
+    gr.Markdown("# The Advanced Multi-Faceted Chatbot")
+    gr.Markdown("Enter text or speak to interact with the chatbot. Choose a feature and specify languages for translation if needed.")
+    
+    with gr.Row():
+        input_text = gr.Textbox(label="Input Text")
+        input_audio = gr.Audio(label="Input Audio", type="filepath")
+    
+    with gr.Row():
+        feature = gr.Radio(["Translation", "Voice Command", "Transcription"], label="Feature")
+        target_language = gr.Textbox(label="Target Language")
+        output_language = gr.Textbox(label="Output Language")
+    
+    submit_button = gr.Button("Process")
+    result_text = gr.Textbox(label="Result")
+    tts_button = gr.Button("Convert to Speech")
+    audio_output = gr.Audio(label="Audio Output")
+
+    submit_button.click(
+        process_input,
+        inputs=[input_text, input_audio, feature, target_language, output_language],
+        outputs=[result_text, audio_output]
+    )
+
+    tts_button.click(
+        tts_function,
+        inputs=[result_text],
+        outputs=[audio_output]
+    )
 
 # Launch the interface
 iface.launch(inline = False)