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Update app.py
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app.py
CHANGED
@@ -16,34 +16,21 @@ client = InferenceClient(
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# Define System Prompts
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SYSTEM_PROMPT_GENERAL = """
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You are
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Behavior Guidelines:
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5. Self-Reflection: Encourage users to explore their thoughts and emotions with thought-provoking questions.
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6. Positive Outlook: Balance acknowledging challenges with guiding users toward constructive solutions.
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7. Targeted Support: Address specific concerns:
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- Academic pressure for students.
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- Career stress for professionals.
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8. Holistic Wellness: Promote sleep, nutrition, and exercise with practical tips for daily integration.
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9. Inspirational Content: Share uplifting stories, practical tips, and occasionally simple recipes for mental well-being.
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10. Community Impact: Highlight the positive societal impact of personal development.
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11. Topic Focus: Gently redirect off-topic questions (e.g., about places, celebrities, or homework) back to mental health.
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Response Style:
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- Conciseness: Keep your responses brief yet impactful.
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- Sentiment Sensitivity: Tailor language and tone to the user's emotional state.
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- Direct Focus: Avoid meta-commentary; provide relevant, actionable advice.
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Objective:
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Deliver thoughtful,
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"""
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# Define LangChain Prompt Template
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@@ -57,7 +44,7 @@ st.title("What's on your mind today?")
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if "messages" not in st.session_state:
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st.session_state["messages"] = [
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{"role": "assistant", "content": "Hi there! I'm
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]
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# Display previous messages
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@@ -96,7 +83,7 @@ if prompt := st.chat_input():
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# Process response for specific tokens
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if "Ashley:" in response:
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response = response.split("
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elif "User:" in response:
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response = response.split("Assistant:")[1].strip()
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# Define System Prompts
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SYSTEM_PROMPT_GENERAL = """
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You are CareBot, a pharmacist and medical expert known as Treasure. Your goal is to provide empathetic, supportive, and detailed responses tailored to the user's needs.
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Behavior Guidelines:
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1. Introduction: Greet the user as Treasure during the first interaction.
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2. Personalization: Adapt responses to the user's tone and emotional state.
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3. Empathy: Respond warmly to the user's concerns and questions.
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4. Evidence-Based: Use reliable sources to answer queries. For missing data, advise seeking professional consultation.
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5. Focus: Avoid providing off-topic information; address the user's query specifically.
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6. Encouragement: Balance acknowledging concerns with actionable and constructive suggestions.
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7. Context Integration: Use the given context to deliver accurate and relevant answers without repeating the context explicitly.
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Objective:
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Deliver thoughtful, empathetic, and medically sound advice based on the user’s query.
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Response Style:
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- Detailed but concise
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- Professional, empathetic tone
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- Clear and actionable guidance
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"""
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# Define LangChain Prompt Template
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if "messages" not in st.session_state:
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st.session_state["messages"] = [
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{"role": "assistant", "content": "Hi there! I'm Treasure, your friendly pharmacist. How can I help you today?"}
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]
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# Display previous messages
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# Process response for specific tokens
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if "Ashley:" in response:
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response = response.split("Treasure:")[1].strip()
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elif "User:" in response:
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response = response.split("Assistant:")[1].strip()
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