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import gradio as gr
from transformers import pipeline
sentiment_model = pipeline("sentiment-analysis")
chatbot_model = pipeline("text-generation", model="microsoft/DialoGPT-medium")
summarization_model = pipeline("summarization")
text_to_speech_model = pipeline("text-to-speech")
def get_sentiment(input_text):
analysis = sentiment_model(input_text)
sent = analysis[0]['label']
score = analysis[0]['score']
return sent, score
def chatbot_response(input_text):
response = chatbot_model(input_text, max_length=100, do_sample=True)[0]['generated_text']
return response
def summarize_text(input_text):
summary = summarization_model(input_text, max_length=100, min_length=30, do_sample=False)
return summary[0]['summary_text']
def text_to_speech(input_text):
audio = text_to_speech_model(input_text)
return audio
with gr.Blocks() as demo:
gr.Markdown("## Multi-Function AI Language Application")
with gr.Tab("Sentiment Analysis"):
text_input = gr.Textbox(label="Enter text for sentiment analysis:")
sentiment_output = gr.Textbox(label="Sentiment")
score_output = gr.Number(label="Confidence Score")
sentiment_button = gr.Button("Analyze")
sentiment_button.click(get_sentiment, inputs=text_input, outputs=[sentiment_output, score_output])
with gr.Tab("Chatbot"):
chat_input = gr.Textbox(label="Enter your message:")
chat_output = gr.Textbox(label="Chatbot Response")
chat_button = gr.Button("Send")
chat_button.click(chatbot_response, inputs=chat_input, outputs=chat_output)
with gr.Tab("Summarization"):
summary_input = gr.Textbox(label="Enter text to summarize:", lines=5)
summary_output = gr.Textbox(label="Summary")
summary_button = gr.Button("Summarize")
summary_button.click(summarize_text, inputs=summary_input, outputs=summary_output)
with gr.Tab("Text-to-Speech"):
tts_input = gr.Textbox(label="Enter text to convert to speech:")
tts_output = gr.Audio(label="Generated Speech")
tts_button = gr.Button("Convert")
tts_button.click(text_to_speech, inputs=tts_input, outputs=tts_output)
# Launch the app
demo.launch() |