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Create app.py
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app.py
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import streamlit as st
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from peft import PeftModel, PeftConfig
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from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
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# Load the chatbot model with PEFT
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@st.cache_resource
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def load_chatbot_model():
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# Load the Peft configuration and base model
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config = PeftConfig.from_pretrained("langtest/falcon-llama3-finetuned-mental-health-hf-plus-dsm5-new-mistral")
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base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
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peft_model = PeftModel.from_pretrained(base_model, "langtest/falcon-llama3-finetuned-mental-health-hf-plus-dsm5-new-mistral")
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# Load the tokenizer for generating the text
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tokenizer = AutoTokenizer.from_pretrained("meta-llama/Meta-Llama-3-8B-Instruct")
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# Create a text generation pipeline using the model and tokenizer
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return pipeline("text-generation", model=peft_model, tokenizer=tokenizer)
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# Initialize the chatbot
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chatbot = load_chatbot_model()
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# Function to generate a response from the chatbot
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def generate_response(user_input):
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# Generate the response using the chatbot model
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response = chatbot(user_input, max_length=100, num_return_sequences=1)
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return response[0]['generated_text']
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# Streamlit UI setup
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st.title("Mental Health Chatbot")
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st.write("""
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This chatbot is designed to provide empathetic responses to mental health issues.
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It is not a replacement for professional help, but it aims to offer support.
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""")
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# Input from the user
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user_input = st.text_input("You: ", placeholder="How are you feeling today?")
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# Display chat history and chatbot responses
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if user_input:
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with st.spinner("The chatbot is thinking..."):
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response = generate_response(user_input)
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st.text_area("Chatbot:", value=response, height=200)
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# Provide some mental health support resources
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st.markdown("""
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### Mental Health Resources:
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- [National Alliance on Mental Illness (NAMI)](https://www.nami.org/Home)
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- [Mental Health America](https://www.mhanational.org/)
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- [Crisis Text Line](https://www.crisistextline.org/)
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""")
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