Delete app.py

app.py

@@ -1,114 +0,0 @@
-!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()