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# import streamlit as st
# from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
# # Load chatbot model
# chatbot_model = "microsoft/DialoGPT-medium"
# tokenizer = AutoTokenizer.from_pretrained(chatbot_model)
# model = AutoModelForCausalLM.from_pretrained(chatbot_model)
# # Load emotion detection model
# emotion_pipeline = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")
# st.title("🧠 Mental Health Chatbot")
# # Chat history
# if "chat_history" not in st.session_state:
# st.session_state.chat_history = []
# # User Input
# user_input = st.text_input("You:", key="user_input")
# if st.button("Send"):
# if user_input:
# # Generate chatbot response
# input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors="pt")
# output = model.generate(input_ids, max_length=200, pad_token_id=tokenizer.eos_token_id)
# response = tokenizer.decode(output[:, input_ids.shape[-1]:][0], skip_special_tokens=True)
# # Detect emotion
# emotion_result = emotion_pipeline(user_input)
# emotion = emotion_result[0]["label"]
# # Store chat history
# st.session_state.chat_history.append(("You", user_input))
# st.session_state.chat_history.append(("Bot", response))
# # Display chat
# for sender, msg in st.session_state.chat_history:
# st.write(f"**{sender}:** {msg}")
# # Display emotion
# st.write(f"🧠 **Emotion Detected:** {emotion}")
import streamlit as st
st.title("🧠 Mental Health Assistant Bot")
# User Input
user_input = st.text_input("How are you feeling today?", "")
if st.button("Submit"):
if user_input:
# Get Emotion Analysis
emotion_result = emotion_pipeline(user_input)[0]
st.write(f"**Emotion Detected:** {emotion_result['label']} ({emotion_result['score']:.2f})")
# Get Mental Health Condition Analysis
mental_health_result = mental_bert_pipeline(user_input)[0]
st.write(f"**Possible Mental Health Condition:** {mental_health_result['label']} ({mental_health_result['score']:.2f})")
# Get Stress Level Analysis
stress_result = stress_pipeline(user_input)[0]
st.write(f"**Stress Level:** {stress_result['label']} ({stress_result['score']:.2f})")
# Chatbot Response using DeepSeek AI
deepseek_response = deepseek_pipeline(user_input, max_length=100, do_sample=True)[0]['generated_text']
st.write(f"🤖 **Chatbot:** {deepseek_response}")
# Question Answering Section
st.subheader("Ask Mental Health Questions")
user_question = st.text_input("Ask me anything about mental health:", "")
if st.button("Ask"):
if user_question:
answer = qa_pipeline(question=user_question, context="Mental health is important for overall well-being.")
st.write(f"**Answer:** {answer['answer']}")
# PHQ-9 Depression Assessment
st.subheader("Depression Severity Assessment (PHQ-9)")
phq9_question = st.text_input("Describe your mood over the last two weeks:", "")
if st.button("Analyze Depression Level"):
if phq9_question:
phq9_result = phq9_pipeline(phq9_question)[0]
st.write(f"**PHQ-9 Score Suggests:** {phq9_result['label']} ({phq9_result['score']:.2f})")
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