app.py

import os
import streamlit as st
import pandas as pd
import faiss
from sentence_transformers import SentenceTransformer
from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
from groq import Groq

# ✅ Set up cache directory
os.environ["HF_HOME"] = "/tmp/huggingface"
os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface"
os.environ["SENTENCE_TRANSFORMERS_HOME"] = "/tmp/huggingface"

# ✅ Load API Key
GROQ_API_KEY = os.getenv("GROQ_API_KEY")
if not GROQ_API_KEY:
    st.error("❌ Error: GROQ_API_KEY is missing. Set it as an environment variable.")
    st.stop()

client = Groq(api_key=GROQ_API_KEY)

# ✅ Load AI Models
st.sidebar.header("Loading AI Models... Please Wait ⏳")
similarity_model = SentenceTransformer("sentence-transformers/all-mpnet-base-v2", cache_folder="/tmp/huggingface")
embedding_model = SentenceTransformer("all-MiniLM-L6-v2", cache_folder="/tmp/huggingface")
summarization_model = AutoModelForSeq2SeqLM.from_pretrained("google/long-t5-tglobal-base", cache_dir="/tmp/huggingface")
summarization_tokenizer = AutoTokenizer.from_pretrained("google/long-t5-tglobal-base", cache_dir="/tmp/huggingface")

# ✅ Load Datasets
try:
    recommendations_df = pd.read_csv("treatment_recommendations.csv")
    questions_df = pd.read_csv("symptom_questions.csv")
except FileNotFoundError as e:
    st.error(f"❌ Missing dataset file: {e}")
    st.stop()

# ✅ FAISS Index for Disorders
treatment_embeddings = similarity_model.encode(recommendations_df["Disorder"].tolist(), convert_to_numpy=True)
index = faiss.IndexFlatIP(treatment_embeddings.shape[1])
index.add(treatment_embeddings)

# ✅ FAISS Index for Questions
question_embeddings = embedding_model.encode(questions_df["Questions"].tolist(), convert_to_numpy=True)
question_index = faiss.IndexFlatL2(question_embeddings.shape[1])
question_index.add(question_embeddings)

# ✅ Retrieve Relevant Question
def retrieve_questions(user_input):
    input_embedding = embedding_model.encode([user_input], convert_to_numpy=True)
    _, indices = question_index.search(input_embedding, 1)

    if indices[0][0] == -1:
        return "I'm sorry, I couldn't find a relevant question."

    return questions_df["Questions"].iloc[indices[0][0]]

# ✅ Generate Empathetic Question
def generate_empathetic_response(user_input, retrieved_question):
    prompt = f"""
    The user said: "{user_input}"
    Relevant Question:
    - {retrieved_question}
    
    You are an empathetic AI psychiatrist. Rephrase this question naturally.
    Example:
    - "I understand that anxiety can be overwhelming. Can you tell me more about when you started feeling this way?"

    Generate only one empathetic response.
    """
    try:
        chat_completion = client.chat.completions.create(
            messages=[{"role": "system", "content": "You are an empathetic AI psychiatrist."},
                      {"role": "user", "content": prompt}],
            model="llama-3.3-70b-versatile",
            temperature=0.8,
            top_p=0.9
        )
        return chat_completion.choices[0].message.content
    except Exception as e:
        return "I'm sorry, I couldn't process your request."

# ✅ Disorder Detection
def detect_disorders(chat_history):
    full_chat_text = " ".join(chat_history)
    text_embedding = similarity_model.encode([full_chat_text], convert_to_numpy=True)
    _, indices = index.search(text_embedding, 3)

    if indices[0][0] == -1:
        return ["No matching disorder found."]

    return [recommendations_df["Disorder"].iloc[i] for i in indices[0]]

# ✅ Summarization
def summarize_chat(chat_history):
    chat_text = " ".join(chat_history)
    inputs = summarization_tokenizer("summarize: " + chat_text, return_tensors="pt", max_length=4096, truncation=True)
    summary_ids = summarization_model.generate(inputs.input_ids, max_length=500, num_beams=4, early_stopping=True)
    return summarization_tokenizer.decode(summary_ids[0], skip_special_tokens=True)

# ✅ UI - Streamlit Chatbot
st.title("MindSpark AI Psychiatrist 💬")

# ✅ Chat History
if "chat_history" not in st.session_state:
    st.session_state.chat_history = []

# ✅ User Input
user_input = st.text_input("You:", "")

if st.button("Send"):
    if user_input:
        retrieved_question = retrieve_questions(user_input)
        empathetic_response = generate_empathetic_response(user_input, retrieved_question)

        st.session_state.chat_history.append(f"User: {user_input}")
        st.session_state.chat_history.append(f"AI: {empathetic_response}")

# ✅ Display Chat History
st.write("### Chat History")
for msg in st.session_state.chat_history[-6:]:  # Show last 6 messages
    st.text(msg)

# ✅ Summarization & Disorder Detection
if st.button("Summarize Chat"):
    summary = summarize_chat(st.session_state.chat_history)
    st.write("### Chat Summary")
    st.text(summary)

if st.button("Detect Disorders"):
    disorders = detect_disorders(st.session_state.chat_history)
    st.write("### Possible Disorders")
    st.text(", ".join(disorders))