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import streamlit as st
from transformers import pipeline, AutoModelForImageClassification, AutoFeatureExtractor
from PIL import Image
import openai
import os
from dotenv import load_dotenv
# =======================
# Load Environment Variables from .env File
# =======================
load_dotenv() # Explicitly load the .env file
# Set OpenAI API key
openai.api_key = os.getenv("OPENAI_API_KEY")
# Debugging: Check if API key is loaded
if not openai.api_key or not openai.api_key.startswith("sk-"):
st.error("OpenAI API key is not set or is invalid. Please check the `.env` file or your environment variable setup.")
st.stop()
# =======================
# Streamlit Page Config
# =======================
st.set_page_config(
page_title="AI-Powered Skin Cancer Detection",
page_icon="🩺",
layout="wide",
initial_sidebar_state="expanded"
)
# =======================
# Load Skin Cancer Model (PyTorch)
# =======================
@st.cache_resource
def load_model():
"""
Load the pre-trained skin cancer classification model using PyTorch.
Use the AutoModelForImageClassification and AutoFeatureExtractor for explicit local caching.
"""
try:
extractor = AutoFeatureExtractor.from_pretrained("Anwarkh1/Skin_Cancer-Image_Classification")
model = AutoModelForImageClassification.from_pretrained("Anwarkh1/Skin_Cancer-Image_Classification")
return pipeline("image-classification", model=model, feature_extractor=extractor, framework="pt")
except Exception as e:
st.error(f"Error loading the model: {e}")
return None
model = load_model()
# =======================
# Generate OpenAI Explanation
# =======================
def generate_openai_explanation(label, confidence):
"""
Generate a detailed explanation for the classification result using OpenAI's GPT model.
"""
prompt = (
f"The AI model has classified an image of a skin lesion as **{label}** with a confidence of **{confidence:.2%}**.\n"
f"Explain what this classification means, including potential characteristics of this lesion type, "
f"what steps a patient should take next, and how the AI might have arrived at this conclusion. "
f"Use language that is easy for a non-medical audience to understand."
)
try:
response = openai.Completion.create(
model="text-davinci-003", # Replace with "gpt-4" if available
prompt=prompt,
max_tokens=300,
temperature=0.7
)
return response.choices[0].text.strip()
except Exception as e:
return f"Error generating explanation: {e}"
# =======================
# Streamlit App Title and Sidebar
# =======================
st.title("🔍 AI-Powered Skin Cancer Classification and Explanation")
st.write("Upload an image of a skin lesion, and the AI model will classify it and provide a detailed explanation.")
st.sidebar.info("""
**AI Cancer Detection Platform**
This application uses AI to classify skin lesions and generate detailed explanations for informational purposes.
It is not intended for medical diagnosis. Always consult a healthcare professional for medical advice.
""")
# =======================
# File Upload and Prediction
# =======================
uploaded_image = st.file_uploader("Upload a skin lesion image (PNG, JPG, JPEG)", type=["png", "jpg", "jpeg"])
if uploaded_image:
# Display uploaded image
image = Image.open(uploaded_image).convert("RGB")
st.image(image, caption="Uploaded Image", use_column_width=True)
# Perform classification
if model is None:
st.error("Model could not be loaded. Please try again later.")
else:
with st.spinner("Classifying the image..."):
try:
results = model(image)
label = results[0]['label']
confidence = results[0]['score']
# Display prediction results
st.markdown(f"### Prediction: **{label}**")
st.markdown(f"### Confidence: **{confidence:.2%}**")
# Provide confidence-based insights
if confidence >= 0.8:
st.success("High confidence in the prediction.")
elif confidence >= 0.5:
st.warning("Moderate confidence in the prediction. Consider additional verification.")
else:
st.error("Low confidence in the prediction. Results should be interpreted with caution.")
# Generate explanation
with st.spinner("Generating a detailed explanation..."):
explanation = generate_openai_explanation(label, confidence)
st.markdown("### Explanation")
st.write(explanation)
except Exception as e:
st.error(f"Error during classification: {e}")
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