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"""Untitled9.ipynb |
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Automatically generated by Colab. |
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Original file is located at |
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https://colab.research.google.com/drive/1lByo8bABlmF1g_sMH-Aps0X2jAab758m |
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""" |
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!pip install gradio --quiet |
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!pip install transformers --quiet |
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import gradio as gr |
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from transformers import pipeline |
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def sentiment_analysis(text): |
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sentiment_pipeline = pipeline("sentiment-analysis") |
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result = sentiment_pipeline(text)[0] |
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return result["label"], result["score"] |
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def chatbot_response(user_input): |
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chatbot = pipeline("text-generation", model="microsoft/DialoGPT-medium") |
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response = chatbot(user_input, max_length=100)[0]["generated_text"] |
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return response |
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def summarize_text(text): |
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summarization_pipeline = pipeline("summarization") |
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summary = summarization_pipeline(text, max_length=100, min_length=30, do_sample=False)[0]["summary_text"] |
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return summary |
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def text_to_speech(text): |
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tts_pipeline = pipeline("text-to-speech", model="facebook/fastspeech2-en-ljspeech") |
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audio = tts_pipeline(text) |
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return audio["audio"] |
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with gr.Blocks() as demo: |
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with gr.Tab("Sentiment Analysis"): |
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text_input = gr.Textbox(label="Enter text") |
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sentiment_output = gr.Textbox(label="Sentiment") |
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confidence_output = gr.Number(label="Confidence Score") |
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sentiment_btn = gr.Button("Analyze") |
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sentiment_btn.click(sentiment_analysis, inputs=text_input, outputs=[sentiment_output, confidence_output]) |
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with gr.Tab("Chatbot"): |
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chat_input = gr.Textbox(label="Ask the chatbot") |
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chat_output = gr.Textbox(label="Response") |
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chat_btn = gr.Button("Send") |
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chat_btn.click(chatbot_response, inputs=chat_input, outputs=chat_output) |
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with gr.Tab("Summarization"): |
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text_area = gr.Textbox(label="Enter long text", lines=5) |
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summary_output = gr.Textbox(label="Summary") |
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summary_btn = gr.Button("Summarize") |
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summary_btn.click(summarize_text, inputs=text_area, 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_btn = gr.Button("Convert") |
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tts_btn.click(text_to_speech, inputs=tts_input, outputs=tts_output) |
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demo.launch() |
