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Update app.py
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app.py
CHANGED
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import streamlit as st
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st.title("π§ Mental Health Assistant Bot")
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# User Input
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user_input = st.text_input("How are you feeling today?", "")
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if st.button("Submit"):
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if user_input:
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#
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emotion_result = emotion_pipeline(user_input)[0]
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st.write(f"**Emotion Detected:** {emotion_result['label']} ({emotion_result['score']:.2f})")
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#
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mental_health_result = mental_bert_pipeline(user_input)[0]
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st.write(f"**Possible Mental Health Condition:** {mental_health_result['label']} ({mental_health_result['score']:.2f})")
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# Get Stress Level Analysis
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stress_result = stress_pipeline(user_input)[0]
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st.write(f"**Stress Level:** {stress_result['label']} ({stress_result['score']:.2f})")
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#
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deepseek_response = deepseek_pipeline(user_input, max_length=100, do_sample=True)[0]['generated_text']
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st.write(f"π€ **Chatbot:** {deepseek_response}")
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# Question Answering Section
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st.subheader("Ask Mental Health Questions")
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user_question = st.text_input("Ask me anything about mental health:", "")
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if st.button("Ask"):
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if user_question:
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answer = qa_pipeline(question=user_question, context="Mental health is important for overall well-being.")
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st.write(f"**Answer:** {answer['answer']}")
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# PHQ-9 Depression Assessment
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st.subheader("Depression Severity Assessment (PHQ-9)")
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phq9_question = st.text_input("Describe your mood over the last two weeks:", "")
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if st.button("Analyze Depression Level"):
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if phq9_question:
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phq9_result = phq9_pipeline(phq9_question)[0]
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st.write(f"**PHQ-9 Score Suggests:** {phq9_result['label']} ({phq9_result['score']:.2f})")
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import streamlit as st
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from transformers import pipeline, AutoTokenizer
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# β
Load Emotion Recognition Model
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emotion_pipeline = pipeline("text-classification", model="distilbert-base-uncased-finetuned-emotion")
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# β
Load Stress Detection Model
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stress_pipeline = pipeline("text-classification", model="mental-health-roberta")
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# β
Load Mental Disorder Detection Model
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mental_bert_pipeline = pipeline("text-classification", model="mental-bert")
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# β
Load PHQ-9 Depression Severity Classifier
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phq9_pipeline = pipeline("text-classification", model="PHQ-9 Depression Classifier")
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# β
Load Chatbot Model (DeepSeek)
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deepseek_model = "deepseek-ai/deepseek-llm-7b"
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deepseek_tokenizer = AutoTokenizer.from_pretrained(deepseek_model)
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deepseek_pipeline = pipeline("text-generation", model=deepseek_model, tokenizer=deepseek_tokenizer)
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# π₯ Streamlit UI
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st.title("π§ Mental Health Assistant Bot")
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user_input = st.text_input("How are you feeling today?", "")
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if st.button("Submit"):
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if user_input:
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# β
Emotion Analysis
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emotion_result = emotion_pipeline(user_input)[0]
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st.write(f"**Emotion Detected:** {emotion_result['label']} ({emotion_result['score']:.2f})")
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# β
Stress Level Analysis
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stress_result = stress_pipeline(user_input)[0]
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st.write(f"**Stress Level:** {stress_result['label']} ({stress_result['score']:.2f})")
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# β
Mental Health Condition Detection
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mental_health_result = mental_bert_pipeline(user_input)[0]
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st.write(f"**Possible Mental Health Condition:** {mental_health_result['label']} ({mental_health_result['score']:.2f})")
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# β
AI Chatbot Response
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deepseek_response = deepseek_pipeline(user_input, max_length=100, do_sample=True)[0]['generated_text']
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st.write(f"π€ **Chatbot:** {deepseek_response}")
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