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from transformers import pipeline |
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import gradio as gr |
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asr = pipeline("automatic-speech-recognition", "facebook/wav2vec2-base-960h") |
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classifier = pipeline("text-classification", "michellejieli/emotion_text_classifier") |
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def transcribe(speech, state=""): |
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text = asr(speech)["text"] |
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state += text + " " |
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return text, state |
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def speech_to_text(speech): |
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text = asr(speech)["text"] |
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return text |
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def text_to_sentiment(text): |
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return classifier(text)[0]["label"] |
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demo = gr.Blocks() |
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with demo: |
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microphone = gr.Audio(source="microphone", type="filepath") |
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audio_file = gr.Audio(type="filepath") |
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text = gr.Textbox() |
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label = gr.Label() |
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b0 = gr.Button("Speech From Microphone") |
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b1 = gr.Button("Recognize Speech") |
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b2 = gr.Button("Classify Sentiment") |
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b0.click(transcribe, inputs=[microphone], outputs=[text]) |
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b1.click(speech_to_text, inputs=audio_file, outputs=text) |
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b2.click(text_to_sentiment, inputs=text, outputs=label) |
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gr.Markdown("""References: |
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## Building an Asynchronous Real-Time Live Telemedicine System Using AI Pipelines for Smart Communities |
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1. **Designing the Telemedicine System** |
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- Identify the needs and challenges of smart communities and design a telemedicine system that addresses these challenges. |
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- Choose a platform that allows for asynchronous real-time communication, such as video conferencing or chat-based messaging, to facilitate remote consultations with healthcare providers. |
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- Design the system to incorporate AI pipelines that can analyze patient data and provide decision support for healthcare providers. |
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2. **Implementing the AI Pipelines** |
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- Identify the relevant AI algorithms and techniques that can be used to analyze patient data, such as machine learning or natural language processing. |
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- Integrate these AI pipelines into the telemedicine system to provide decision support for healthcare providers during consultations. |
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- Ensure that the AI algorithms are accurate and reliable by testing them on a large and diverse set of patient data. |
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3. **Deploying the Telemedicine System** |
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- Deploy the telemedicine system in smart communities, ensuring that it is easily accessible and user-friendly for patients and healthcare providers. |
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- Train healthcare providers on how to use the system effectively and provide ongoing support and feedback to optimize its use. |
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- Continuously monitor and evaluate the system's performance, making improvements and updates as needed to ensure that it remains effective and efficient in meeting the needs of smart communities. |
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**__Asynchronous Telemedicine:__ A Solution to Address Provider Shortages by Offering Remote Care Services.** |
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([Wikipedia](https://en.wikipedia.org/wiki/Telemedicine)) |
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# 2023's Top 7 Breakthroughs in Medical Technology |
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1. __Asynchronous Telemedicine:__ A Solution to Address Provider Shortages by Offering Remote Care Services. ([Wikipedia](https://en.wikipedia.org/wiki/Telemedicine)) |
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2. __Ambient and Emotion AI:__ Empowering Patients with Artificial Intelligence That Shows Empathy and Compassion. ([Wikipedia](https://en.wikipedia.org/wiki/Ambient_intelligence)) |
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3. __Skin Patch Technology:__ A Convenient Way to Measure Vital Signals such as Blood Pressure and Glucose Levels. ([Wikipedia](https://en.wikipedia.org/wiki/Skin_patch)) |
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4. __Affordable Vein Scanner:__ A Revolutionary Tool to View Veins Through the Skin. ([Wikipedia](https://en.wikipedia.org/wiki/Vein_matching)) |
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5. __Synthetic Medical Records:__ Creating Reliable Medical Records Using Generative Adversarial Networks. ([Wikipedia](https://en.wikipedia.org/wiki/Synthetic_data)) |
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6. __Blood Draw Devices for Clinical Trials:__ Facilitating Remote Participation in Trials with Innovative Technology. ([Wikipedia](https://en.wikipedia.org/wiki/Blood_sampling)) |
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7. __Smart TVs for Remote Care:__ Enhancing Remote Care Consultations with Video Chat and Recordings. ([Wikipedia](https://en.wikipedia.org/wiki/Smart_television)) |
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Reference: [The Medical Futurist](https://www.youtube.com/watch?v=_9DpLD4S2AY&list=PLHgX2IExbFotoMt32SrT3Xynt5BXTGnEP&index=2) |
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""") |
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demo.launch() |
