app.py

import torch
import random
import numpy as np
torch.manual_seed(42)
random.seed(42)
np.random.seed(42)
import streamlit as st
import io
from PIL import Image
import os
from transformers import logging
from SkinGPT import SkinGPTClassifier
from fpdf import FPDF
import nest_asyncio
nest_asyncio.apply()
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")

import re


def remove_code_blocks(text):
    # Remove triple backtick code blocks
    text = re.sub(r"```[\s\S]*?```", "", text)
    # Remove lines that start with 4 or more spaces (Markdown indented code blocks)
    text = re.sub(r"^( {4,}.*\n?)+", "", text, flags=re.MULTILINE)
    return text

device='cuda' if torch.cuda.is_available() else 'cpu'
st.set_page_config(page_title="SkinGPT", page_icon="🧬", layout="centered")


@st.cache_resource(show_spinner=False)
def get_classifier():
    classifier = SkinGPTClassifier()
    for module in [classifier.model.vit,
                   classifier.model.q_former,
                   classifier.model.llama]:
        module.eval()
        for param in module.parameters():
            param.requires_grad = False

    return classifier

if 'app_models' not in st.session_state:
    st.session_state.app_models = get_classifier()

classifier = st.session_state.app_models

# === Session Init ===
if "messages" not in st.session_state:
    st.session_state.messages = []

if "current_image" not in st.session_state:
    st.session_state.current_image = None

# === 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_bytes = pdf.output(dest='S').encode('latin1')
    buf.write(pdf_bytes)
    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"],
    key="file_uploader"
)

if uploaded_file is not None and uploaded_file != st.session_state.current_image:
    st.session_state.messages = []
    st.session_state.current_image = uploaded_file
    classifier.current_image_embeddings = None

    image = Image.open(uploaded_file).convert("RGB")
    st.image(image, caption="Uploaded image", use_column_width=True)
    with st.spinner("Analyzing the image..."):
        result = classifier.predict(image, reuse_embeddings=False)
        print("result in app : ", result["diagnosis"])
    st.session_state.messages.append({"role": "assistant", "content": result["diagnosis"]})

for message in st.session_state.messages:
    with st.chat_message(message["role"]):
        # st.markdown(remove_code_blocks(message["content"]))
        st.markdown(message["content"])
        # st.text(message["content"])

# for message in st.session_state.messages:
#     role = "You" if message["role"] == "user" else "assistant"
#     st.markdown(f"**{role}:** {message['content']}")

# === Chat Interface ===
if prompt := st.chat_input("Ask a follow-up question..."):
    st.session_state.messages.append({"role": "user", "content": prompt})
    with st.chat_message("user"):
        st.markdown(remove_code_blocks(prompt))

    # st.markdown(f"**You:** {prompt}")

    with st.chat_message("assistant"):
        with st.spinner("Thinking..."):
            image = Image.open(st.session_state.current_image).convert("RGB")
            if len(st.session_state.messages) > 1:
                conversation_context = "\n".join(
                    f"{m['role']}: {m['content']}"
                    for m in st.session_state.messages[:-1]
                )
                augmented_prompt = (
                    f"Conversation history:\n{conversation_context}\n\n"
                    f"Current question: {prompt}"
                )
                result = classifier.predict(image, user_input=augmented_prompt, reuse_embeddings=True)
            else:
                result = classifier.predict(image, user_input=prompt, reuse_embeddings=False)

            # st.markdown(remove_code_blocks(result["diagnosis"]))
            st.markdown(result["diagnosis"])
            # st.text(result["diagnosis"])
            st.session_state.messages.append({"role": "assistant", "content": result["diagnosis"]})

if st.session_state.messages and 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="skingpt_chat_history.pdf",
        mime="application/pdf"
    )