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