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Update app.py
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app.py
CHANGED
@@ -1,6 +1,6 @@
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import streamlit as st
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#from transformers import AutoTokenizer
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from llama_cpp import Llama
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from transformers import pipeline
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#from peft import PeftModel, PeftConfig
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#from transformers import AutoModelForCausalLM
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@@ -51,7 +51,7 @@ st.markdown('<div class="blurred-background"></div>', unsafe_allow_html=True)
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@st.cache_resource
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def load_text_generation_model():
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try:
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return pipeline("text-generation", model="
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except Exception as e:
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st.error(f"Error loading model: {e}")
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return None
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@@ -69,7 +69,7 @@ dataset = load_counseling_dataset()
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st.title("Mental Health Counseling Chat")
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st.markdown("""
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Welcome to the **Mental Health Counseling Chat Application**.
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This platform is designed to provide **supportive, positive, and encouraging responses**
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""")
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# Check if the model loaded correctly
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@@ -95,7 +95,7 @@ else:
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response = text_generator(prompt, max_length=200, num_return_sequences=1)
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# Extract and display the response
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counselor_reply = response[0]["generated_text"].
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st.subheader("Counselor's Response:")
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st.write(counselor_reply)
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except Exception as e:
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@@ -111,6 +111,4 @@ else:
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- [National Suicide Prevention Lifeline](https://suicidepreventionlifeline.org)
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""")
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st.sidebar.info("This application is not a replacement for professional counseling. If you are in crisis, please seek professional help immediately.")
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-
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-
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import streamlit as st
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#from transformers import AutoTokenizer
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#from llama_cpp import Llama
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from transformers import pipeline
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#from peft import PeftModel, PeftConfig
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#from transformers import AutoModelForCausalLM
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@st.cache_resource
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def load_text_generation_model():
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try:
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return pipeline("text-generation", model="google/gemma-2-9b-it")
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except Exception as e:
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st.error(f"Error loading model: {e}")
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return None
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st.title("Mental Health Counseling Chat")
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st.markdown("""
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Welcome to the **Mental Health Counseling Chat Application**.
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This platform is designed to provide **supportive, positive, and encouraging responses** using a fast and efficient language model.
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""")
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# Check if the model loaded correctly
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response = text_generator(prompt, max_length=200, num_return_sequences=1)
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# Extract and display the response
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counselor_reply = response[0]["generated_text"].strip()
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st.subheader("Counselor's Response:")
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st.write(counselor_reply)
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except Exception as e:
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- [National Suicide Prevention Lifeline](https://suicidepreventionlifeline.org)
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""")
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st.sidebar.info("This application is not a replacement for professional counseling. If you are in crisis, please seek professional help immediately.")
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