Create app.py
Browse files
app.py
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import gradio as gr
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from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
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import torch
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# Load model and tokenizer
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model_name = "ombhojane/mental-health-assistant"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
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def generate_response(message, history):
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# Format the input with chat history
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prompt = ""
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for user_msg, bot_msg in history:
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prompt += f"User: {user_msg}\nAssistant: {bot_msg}\n"
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prompt += f"User: {message}\nAssistant:"
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# Generate response
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response = pipe(
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prompt,
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max_length=256,
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num_return_sequences=1,
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temperature=0.7,
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top_p=0.9,
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do_sample=True,
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pad_token_id=tokenizer.eos_token_id
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)[0]["generated_text"]
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# Extract just the assistant's response
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try:
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assistant_response = response.split("Assistant:")[-1].strip()
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except:
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assistant_response = response
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return assistant_response
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# Create the Gradio interface
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with gr.Blocks(css="footer {visibility: hidden}") as demo:
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gr.Markdown(
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"""
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# 🧠 Mental Health Assistant
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Welcome! I'm here to provide support and guidance for your mental health concerns.
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While I can offer helpful insights, please remember I'm not a replacement for professional medical advice.
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"""
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)
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chatbot = gr.Chatbot(
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height=400,
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show_label=False,
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container=True,
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bubble_full_width=False,
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)
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with gr.Row():
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msg = gr.Textbox(
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scale=4,
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show_label=False,
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placeholder="Type your message here...",
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container=False
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)
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submit = gr.Button("Send", scale=1, variant="primary")
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gr.Examples(
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examples=[
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"I've been feeling really anxious lately",
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"How can I improve my sleep habits?",
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"I'm having trouble focusing at work",
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],
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inputs=msg
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)
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gr.Markdown(
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"""
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### Tips for best results:
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- Be specific about how you're feeling
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- Ask direct questions
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- Share relevant context
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- Take your time to explain your situation
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"""
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)
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# Set up event handlers
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submit_click = submit.click(
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generate_response,
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inputs=[msg, chatbot],
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outputs=[chatbot],
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queue=True
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)
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submit_click.then(lambda: "", None, msg, queue=False)
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msg.submit(
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generate_response,
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inputs=[msg, chatbot],
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outputs=[chatbot],
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queue=True
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).then(lambda: "", None, msg, queue=False)
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demo.launch()
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