Create app.py

app.py

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+!pip install SpeechRecognition googletrans==3.1.0a0 textblob nltk tensorflow gtts gradio
+
+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)
+nltk.download('stopwords', quiet=True)
+nltk.download('wordnet', quiet=True)
+
+# Initialize components
+recognizer = sr.Recognizer()
+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 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", 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"
+
+    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
+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()