app.py

# 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
from transformers import pipeline, AutoTokenizer

# ✅ Load Emotion Recognition Model
emotion_pipeline = pipeline("text-classification", model="ahmettasdemir/distilbert-base-uncased-finetuned-emotion")

# ✅ Load Stress Detection Model
stress_pipeline = pipeline("text-classification", model="j-hartmann/emotion-english-distilroberta-base")

# ✅ Load Mental Disorder Detection Model
mental_bert_pipeline = pipeline("text-classification", model="nlpconnect/vit-gpt2-image-captioning")

# ✅ Load PHQ-9 Depression Severity Classifier
phq9_pipeline = pipeline("text-classification", model="PHQ-9 Depression Classifier")

# ✅ Load Chatbot Model (DeepSeek)
deepseek_model = "deepseek-ai/deepseek-llm-7b"
deepseek_tokenizer = AutoTokenizer.from_pretrained(deepseek_model)
deepseek_pipeline = pipeline("text-generation", model=deepseek_model, tokenizer=deepseek_tokenizer)

# 🏥 Streamlit UI
st.title("🧠 Mental Health Assistant Bot")

user_input = st.text_input("How are you feeling today?", "")

if st.button("Submit"):
    if user_input:
        # ✅ Emotion Analysis
        emotion_result = emotion_pipeline(user_input)[0]
        st.write(f"**Emotion Detected:** {emotion_result['label']} ({emotion_result['score']:.2f})")

        # ✅ Stress Level Analysis
        stress_result = stress_pipeline(user_input)[0]
        st.write(f"**Stress Level:** {stress_result['label']} ({stress_result['score']:.2f})")

        # ✅ Mental Health Condition Detection
        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})")

        # ✅ AI Chatbot Response
        deepseek_response = deepseek_pipeline(user_input, max_length=100, do_sample=True)[0]['generated_text']
        st.write(f"🤖 **Chatbot:** {deepseek_response}")