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Update app.py
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app.py
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import gradio as gr
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token = choices[0].delta.content
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response += token
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yield response
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"""
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For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
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"""
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chatbot = gr.ChatInterface(
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respond,
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type="messages",
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additional_inputs=[
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gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
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gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
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gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
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gr.Slider(
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minimum=0.1,
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maximum=1.0,
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value=0.95,
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step=0.05,
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label="Top-p (nucleus sampling)",
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),
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],
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)
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with gr.Sidebar():
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gr.LoginButton()
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chatbot.render()
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if __name__ == "__main__":
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demo.launch()
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import gradio as gr
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import requests
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import os
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API_URL = "https://api-inference.huggingface.co/models/azkamannan2004/MindEase-CD-Part21-24"
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headers = {
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"Authorization": f"Bearer {os.getenv('HF_TOKEN')}"
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}
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def chat(message):
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payload = {
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"inputs": message,
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"parameters": {
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"max_new_tokens": 200,
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"temperature": 0.7
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}
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}
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response = requests.post(API_URL, headers=headers, json=payload)
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try:
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result = response.json()
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if isinstance(result, list):
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return result[0]["generated_text"]
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else:
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return str(result)
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except:
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return "Error from model"
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demo = gr.Interface(
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fn=chat,
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inputs=gr.Textbox(lines=2, placeholder="Apna message likho..."),
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outputs="text",
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title="MindEase – Mental Health Chatbot",
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description="Stress & Depression Support (Fine-tuned Model)"
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)
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demo.launch()
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