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import streamlit as st
import torchvision.transforms as transforms
import torch
import io
import os
from fpdf import FPDF
import nest_asyncio
nest_asyncio.apply()
device='cuda' if torch.cuda.is_available() else 'cpu'
st.set_page_config(page_title="DermBOT", page_icon="🧬", layout="centered")
import torch
from torch import nn
from torchvision import transforms
from PIL import Image
from transformers import LlamaForCausalLM, LlamaTokenizer, BertModel, BertConfig
from eva_vit import create_eva_vit_g
import requests
from io import BytesIO
import os
from huggingface_hub import hf_hub_download
from transformers import BitsAndBytesConfig
from accelerate import init_empty_weights
import warnings
from transformers import logging
import torch
from torch.cuda.amp import autocast
from SkinGPT import SkinGPTClassifier
# Set default dtypes
torch.set_default_dtype(torch.float32) # Main computations in float32
MODEL_DTYPE = torch.float16 if torch.cuda.is_available() else torch.float32
import warnings
warnings.filterwarnings("ignore", category=FutureWarning)
warnings.filterwarnings("ignore", category=UserWarning)
logging.set_verbosity_error()
token = os.getenv("HF_TOKEN")
if not token:
raise ValueError("Hugging Face token not found in environment variables")
import warnings
warnings.filterwarnings("ignore")
def get_classifier():
return SkinGPTClassifier()
classifier = get_classifier()
# === Session Init ===
if "messages" not in st.session_state:
st.session_state.messages = []
# === PDF Export ===
def export_chat_to_pdf(messages):
pdf = FPDF()
pdf.add_page()
pdf.set_font("Arial", size=12)
for msg in messages:
role = "You" if msg["role"] == "user" else "AI"
pdf.multi_cell(0, 10, f"{role}: {msg['content']}\n")
buf = io.BytesIO()
pdf.output(buf)
buf.seek(0)
return buf
# === App UI ===
st.title("🧬 DermBOT — Skin AI Assistant")
st.caption(f"🧠 Using model: SkinGPT")
uploaded_file = st.file_uploader("Upload a skin image", type=["jpg", "jpeg", "png"])
if "conversation" not in st.session_state:
st.session_state.conversation = []
if uploaded_file:
st.image(uploaded_file, caption="Uploaded image", use_column_width=True)
image = Image.open(uploaded_file).convert("RGB")
if not st.session_state.conversation:
with st.spinner("Analyzing image..."):
result = classifier.predict(image)
if "error" in result:
st.error(result["error"])
else:
st.session_state.conversation.append(("assistant", result))
with st.chat_message("assistant"):
st.markdown(result["diagnosis"])
else:
# Follow-up questions
if user_query := st.chat_input("Ask a follow-up question..."):
st.session_state.conversation.append(("user", user_query))
with st.chat_message("user"):
st.markdown(user_query)
# Generate response with context
context = "\n".join([f"{role}: {msg}" for role, msg in st.session_state.conversation])
response = classifier.generate(image, user_input=context)
st.session_state.conversation.append(("assistant", response))
with st.chat_message("assistant"):
st.markdown(response)
# === PDF Button ===
if st.button("📄 Download Chat as PDF"):
pdf_file = export_chat_to_pdf(st.session_state.messages)
st.download_button("Download PDF", data=pdf_file, file_name="chat_history.pdf", mime="application/pdf") |