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import streamlit as st
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
from peft import PeftModel
import os
# Page config
st.set_page_config(
page_title="TinyLlama Medical Assistant",
page_icon="🩺",
layout="wide"
)
# Custom CSS
st.markdown("""
<style>
.main {background-color: #f0f2f6;}
.stButton>button {
width: 100%;
background-color: #4F46E5;
color: white;
}
.chat-message {
padding: 1.5rem;
border-radius: 0.5rem;
margin-bottom: 1rem;
display: flex;
flex-direction: column;
}
.chat-message.user {
background-color: #4F46E5;
color: white;
}
.chat-message.assistant {
background-color: white;
border: 1px solid #e5e7eb;
}
</style>
""", unsafe_allow_html=True)
# User credentials
USERS = {
"admin": "admin123",
"doctor": "doc123",
"student": "student123"
}
MEDICAL_DISCLAIMER = """
⚠️ **Medical Disclaimer:** This response is for educational purposes only and is not a substitute for professional medical advice. Always consult a qualified healthcare provider.
"""
# Initialize session state
if "authenticated" not in st.session_state:
st.session_state.authenticated = False
if "messages" not in st.session_state:
st.session_state.messages = []
if "model_loaded" not in st.session_state:
st.session_state.model_loaded = False
# Login page
if not st.session_state.authenticated:
col1, col2, col3 = st.columns([1, 2, 1])
with col2:
st.title("🔐 Medical Assistant Login")
st.markdown("---")
username = st.text_input("Username", key="login_username")
password = st.text_input("Password", type="password", key="login_password")
col_a, col_b = st.columns(2)
with col_a:
if st.button("Login", use_container_width=True):
if username in USERS and USERS[username] == password:
st.session_state.authenticated = True
st.session_state.username = username
st.success("✅ Login successful!")
st.rerun()
else:
st.error("❌ Invalid credentials")
with col_b:
if st.button("Clear", use_container_width=True):
st.rerun()
st.markdown("---")
st.info("""
**Demo Credentials:**
- admin / admin123
- doctor / doc123
- student / student123
""")
st.stop()
# Load model (cached)
@st.cache_resource(show_spinner=False)
def load_model():
"""Load the fine-tuned TinyLlama model with LoRA adapters"""
try:
base_model_name = "TinyLlama/TinyLlama-1.1B-Chat-v1.0"
lora_path = "./tinyllama-medical-lora"
# Check if LoRA weights exist
if not os.path.exists(lora_path):
st.error(f"❌ Model not found at {lora_path}")
st.info("Using base model without fine-tuning...")
lora_path = None
# Quantization config for efficient inference
bnb_config = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_quant_type="nf4",
bnb_4bit_compute_dtype=torch.bfloat16,
bnb_4bit_use_double_quant=True
)
# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(base_model_name)
tokenizer.pad_token = tokenizer.eos_token
# Load base model
model = AutoModelForCausalLM.from_pretrained(
base_model_name,
quantization_config=bnb_config,
device_map="auto",
trust_remote_code=True
)
# Load LoRA adapters if available
if lora_path:
model = PeftModel.from_pretrained(model, lora_path)
st.success("✅ Fine-tuned model loaded successfully!")
else:
st.warning("⚠️ Using base model (not fine-tuned)")
model.eval()
return tokenizer, model
except Exception as e:
st.error(f"Error loading model: {str(e)}")
return None, None
# Main app
st.title("🩺 TinyLlama Medical Assistant")
st.caption(f"Logged in as: **{st.session_state.username}**")
# Sidebar
with st.sidebar:
st.header("⚙️ Settings")
# Model loading status
if not st.session_state.model_loaded:
with st.spinner("Loading fine-tuned model..."):
tokenizer, model = load_model()
if tokenizer and model:
st.session_state.tokenizer = tokenizer
st.session_state.model = model
st.session_state.model_loaded = True
st.markdown("---")
# Generation parameters
st.subheader("Generation Parameters")
temperature = st.slider("Temperature", 0.1, 1.5, 0.7, 0.1)
max_tokens = st.slider("Max New Tokens", 32, 256, 100, 8)
top_p = st.slider("Top-p", 0.1, 1.0, 0.9, 0.05)
st.markdown("---")
# Example queries
st.subheader("💡 Example Queries")
example_queries = [
"What is Paracetamol used for?",
"Tell me about Ibuprofen",
"What is Metformin?",
"Uses of Amoxicillin",
"What is Atorvastatin for?"
]
for query in example_queries:
if st.button(query, key=f"example_{query}", use_container_width=True):
st.session_state.messages.append({"role": "user", "content": query})
st.rerun()
st.markdown("---")
# Clear chat
if st.button("🗑️ Clear Chat", use_container_width=True):
st.session_state.messages = []
st.rerun()
# Logout
if st.button("🚪 Logout", use_container_width=True):
st.session_state.authenticated = False
st.session_state.messages = []
st.rerun()
# Display chat messages
for message in st.session_state.messages:
with st.chat_message(message["role"]):
st.markdown(message["content"])
# Chat input
if prompt := st.chat_input("Ask a medical question..."):
# Add user message
st.session_state.messages.append({"role": "user", "content": prompt})
with st.chat_message("user"):
st.markdown(prompt)
# Generate response
with st.chat_message("assistant"):
with st.spinner("Thinking..."):
if st.session_state.model_loaded:
try:
# Format prompt
formatted_prompt = f"""### Instruction:
{prompt}
### Response:
"""
# Tokenize
inputs = st.session_state.tokenizer(
formatted_prompt,
return_tensors="pt"
).to(st.session_state.model.device)
# Generate
with torch.no_grad():
outputs = st.session_state.model.generate(
**inputs,
max_new_tokens=max_tokens,
temperature=temperature,
top_p=top_p,
do_sample=True,
pad_token_id=st.session_state.tokenizer.eos_token_id
)
# Decode
response = st.session_state.tokenizer.decode(
outputs[0],
skip_special_tokens=True
)
# Extract only the response part
if "### Response:" in response:
response = response.split("### Response:")[-1].strip()
# Add disclaimer
full_response = f"{response}\n\n{MEDICAL_DISCLAIMER}"
st.markdown(full_response)
st.session_state.messages.append({
"role": "assistant",
"content": full_response
})
except Exception as e:
error_msg = f"Error generating response: {str(e)}"
st.error(error_msg)
st.session_state.messages.append({
"role": "assistant",
"content": error_msg
})
else:
error_msg = "Model not loaded. Please refresh the page."
st.error(error_msg)
st.session_state.messages.append({
"role": "assistant",
"content": error_msg
})
# Footer
st.markdown("---")
st.caption("Fine-tuned TinyLlama 1.1B with LoRA on Allopathic Medicine Dataset") |