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 cache directory
os.environ["HF_HOME"] = "/tmp/huggingface"
os.environ["TRANSFORMERS_CACHE"] = "/tmp/huggingface"
os.environ["SENTENCE_TRANSFORMERS_HOME"] = "/tmp/huggingface"

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

client = Groq(api_key=GROQ_API_KEY)

# ✅ Load AI Models
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 Disorder Detection
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 Question Retrieval
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)

# ✅ Function: Retrieve the most 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."
    question_block = questions_df["Questions"].iloc[indices[0][0]]
    return question_block.split(", ")[0] if ", " in question_block else question_block

# ✅ Function: Generate empathetic response using Groq API
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 in a human-like way.
    """
    try:
        response = client.chat.completions.create(
            messages=[
                {"role": "system", "content": "You are a helpful, empathetic AI psychiatrist."},
                {"role": "user", "content": prompt}
            ],
            model="llama-3.3-70b-versatile",
            temperature=0.8,
            top_p=0.9
        )
        return response.choices[0].message.content
    except Exception as e:
        return "I'm sorry, I couldn't process your request."
# ✅ Function to detect disorders
def detect_disorders(chat_history):
    """Detect psychiatric disorders from full chat history."""
    full_chat_text = " ".join(chat_history)
    text_embedding = similarity_model.encode([full_chat_text], convert_to_numpy=True)
    distances, indices = index.search(text_embedding, 3)

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

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

# ✅ Function to get treatment recommendations
def get_treatment(detected_disorders):
    """Retrieve treatment recommendations based on detected disorders."""
    treatments = {
        disorder: recommendations_df[recommendations_df["Disorder"] == disorder]["Treatment Recommendation"].values[0]
        for disorder in detected_disorders if disorder in recommendations_df["Disorder"].values
    }
    return treatments


# ✅ Streamlit UI Setup
st.title("🧠 MindSpark AI Psychiatric Assistant")

chat_history = st.session_state.get("chat_history", [])
user_input = st.text_input("Enter your message:")

if st.button("Ask AI") and user_input:
    retrieved_question = retrieve_questions(user_input)
    empathetic_response = generate_empathetic_response(user_input, retrieved_question)
    chat_history.append(f"User: {user_input}")
    chat_history.append(f"AI: {empathetic_response}")
    st.session_state["chat_history"] = chat_history

st.subheader("Chat History")
for msg in chat_history:
    st.write(msg)

if st.button("Summarize Chat"):
    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)
    summary = summarization_tokenizer.decode(summary_ids[0], skip_special_tokens=True)
    st.subheader("Chat Summary")
    st.write(summary)
if st.button("Detect Disorders"):
    if chat_history.strip():
        disorders = detect_disorders(chat_history.split("\n"))
        st.write("**Detected Disorders:**")
        for disorder in disorders:
            st.write(f"- {disorder}")
    else:
        st.error("❌ Please enter chat history.")

if st.button("Get Treatment Recommendations"):
    if chat_history.strip():
        detected_disorders = detect_disorders(chat_history.split("\n"))
        treatments = get_treatment(detected_disorders)
        st.write("**Treatment Recommendations:**")
        for disorder, treatment in treatments.items():
            st.write(f"**{disorder}:** {treatment}")
    else:
        st.error("❌ Please enter chat history.")