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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."
# ✅ 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)
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