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import os
import torch
import streamlit as st
from PIL import Image
from transformers import AutoModelForImageClassification, AutoImageProcessor
from groq import Groq
# Set page config
st.set_page_config(page_title="DermaBot - AI Skin Disease Detector", page_icon="🩺", layout="wide")
# Load model and processor
MODEL_NAME = "Jayanth2002/dinov2-base-finetuned-SkinDisease"
DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
model = AutoModelForImageClassification.from_pretrained(MODEL_NAME).to(DEVICE)
processor = AutoImageProcessor.from_pretrained(MODEL_NAME)
# Set up the Groq API key (replace with your actual key or use an environment variable)
client = Groq(api_key="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")
# Initialize session state for disease details
if "disease_name" not in st.session_state:
st.session_state.disease_name = None
if "disease_info" not in st.session_state:
st.session_state.disease_info = None
# Function to predict skin disease
def predict_skin_disease(image):
image = image.convert("RGB")
inputs = processor(images=image, return_tensors="pt").to(DEVICE)
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
predicted_class_idx = logits.argmax(-1).item()
predicted_label = model.config.id2label[predicted_class_idx]
return predicted_label
# Function to get disease details from Groq API
def get_disease_info(disease_name):
prompt = f"Provide a detailed explanation about the skin disease '{disease_name}', including description of disease, causes, precausions, risk and treatment options."
chat_completion = client.chat.completions.create(
messages=[{"role": "user", "content": prompt}],
model="llama-3.3-70b-versatile",
)
return chat_completion.choices[0].message.content
# Function to handle chatbot queries
def chatbot_response(disease_name, user_query):
if not disease_name:
return "Please upload an image and detect the disease first."
prompt = f"The detected skin disease is '{disease_name}'. {user_query}"
chat_completion = client.chat.completions.create(
messages=[{"role": "user", "content": prompt}],
model="llama-3.3-70b-versatile",
)
return chat_completion.choices[0].message.content
# Streamlit UI
st.image("https://huggingface.co/spaces/your-huggingface-space/logo.png", width=200)
st.title("🩺 DermaBot - AI Skin Disease Detector")
st.write("Upload an image of a skin condition to get a diagnosis and ask questions about it.")
# Step 1: Upload image
uploaded_image = st.file_uploader("Upload an image", type=["jpg", "jpeg", "png"])
if uploaded_image:
image = Image.open(uploaded_image)
st.image(image, caption="Uploaded Image", use_container_width=True)
# Step 2: Detect disease
if st.button("Detect Disease"):
with st.spinner("Analyzing..."):
disease_name = predict_skin_disease(image)
disease_info = get_disease_info(disease_name)
# Store results in session state
st.session_state.disease_name = disease_name
st.session_state.disease_info = disease_info
# Display detected disease information if available
if st.session_state.disease_name:
st.success(f"**Detected Disease:** {st.session_state.disease_name}")
st.write(f"**Details:** {st.session_state.disease_info}")
# Step 3: Chatbot
st.subheader("💬 Ask DermaBot about this disease")
user_query = st.text_input("Ask about the detected disease:")
if st.button("Ask"):
with st.spinner("Thinking..."):
response = chatbot_response(st.session_state.disease_name, user_query)
st.write(response)
st.markdown("---")
st.write("🔍 Powered by **AI & Groq API** | © 2025 DermaBot")
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