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app.py

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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:]}")

requirements.txt

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+streamlit
+transformers
+torch
+tensorflow-cpu==2.11.0