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import streamlit as st
import numpy as np
import os
from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
from tensorflow.keras.models import load_model
from tensorflow.keras.preprocessing.text import tokenizer_from_json
from tensorflow.keras.preprocessing.sequence import pad_sequences
import json
# Load tokenizer from file
with open("tokenizer.json") as f:
data = json.load(f)
from tensorflow.keras.preprocessing.text import tokenizer_from_json
tokenizer = tokenizer_from_json(data)
# Preprocessing function
def preprocess(text):
sequence = tokenizer.texts_to_sequences([text])
return pad_sequences(sequence, maxlen=100)
# Load Keras models
model1 = load_model("model1.h5")
model2 = load_model("model2.h5")
def model1_predict(text):
pred = model1.predict(preprocess(text))[0][0]
return int(pred > 0.5)
def model2_predict(text):
pred = model2.predict(preprocess(text))[0]
return int(np.argmax(pred))
diagnosis_labels = {
1: "Anxiety",
2: "Depression",
3: "Bipolar disorder",
4: "PTSD",
5: "OCD",
6: "ADHD",
7: "General emotional distress"
}
@st.cache_resource
def load_llm():
model_id = "tiiuae/falcon-7b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto",
trust_remote_code=True,
torch_dtype="auto"
)
return pipeline("text-generation", model=model, tokenizer=tokenizer, device_map="auto")
generator = load_llm()
if "history" not in st.session_state:
st.session_state.history = []
def therapist_pipeline(user_input):
st.session_state.history.append(f"User: {user_input}")
risk = model1_predict(user_input)
if risk == 1:
response = (
"I'm really sorry you're feeling this way. You're not alone — please talk to someone you trust "
"or a professional. I'm here to listen, but it's important to get real support too. 💙"
)
else:
diagnosis_code = model2_predict(user_input)
diagnosis = diagnosis_labels.get(diagnosis_code, "General emotional distress")
prompt = f"""You are an empathetic AI therapist. The user has been diagnosed with {diagnosis}. Respond supportively.
User: {user_input}
AI:"""
response = generator(prompt, max_new_tokens=150, temperature=0.7)[0]["generated_text"]
response = response.split("AI:")[-1].strip()
st.session_state.history.append(f"AI: {response}")
return response
def summarize_session():
session_text = "\n".join(st.session_state.history)
prompt = f"""Summarize the emotional state of the user based on the following conversation. Include emotional cues and possible diagnoses. Write it like a therapist note.
Conversation:
{session_text}
Summary:"""
summary = generator(prompt, max_new_tokens=250, temperature=0.5)[0]["generated_text"]
return summary.split("Summary:")[-1].strip()
st.title("🧠 TARS.help - AI Therapist")
user_input = st.text_input("How are you feeling today?")
if user_input:
response = therapist_pipeline(user_input)
st.markdown(f"**AI Therapist:** {response}")
if st.button("🧾 Generate Therapist Summary"):
st.markdown("### 🧠 Session Summary")
st.markdown(summarize_session())
for i in range(0, len(st.session_state.history), 2):
st.markdown(f"**You:** {st.session_state.history[i][6:]}")
st.markdown(f"**AI:** {st.session_state.history[i+1][4:]}")
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