app.py

import streamlite 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}")