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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})") | |