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# app.py
import gradio as gr
from PIL import Image
import torch
import pandas as pd
from transformers import AutoImageProcessor, AutoModelForImageClassification
# Load model
model_name = "Anwarkh1/Skin_Cancer-Image_Classification"
processor = AutoImageProcessor.from_pretrained(model_name)
model = AutoModelForImageClassification.from_pretrained(model_name)
label_map = model.config.id2label
# Match labels exactly from model
condition_info = {
"actinic_keratoses": "Dry, rough patch – sometimes early sign of skin cancer.",
"basal_cell_carcinoma": "Slow-growing skin cancer. Common but treatable.",
"benign_keratosis-like_lesions": "Non-cancerous growth. Like age spots or warts.",
"dermatofibroma": "Small, firm bump. Usually harmless.",
"melanocytic_nevi": "Just a mole. Normal unless changing.",
"melanoma": "Dangerous skin cancer. Needs fast treatment.",
"vascular_lesions": "Red or purple patches from blood vessels."
}
# AI prediction logic
def classify_skin(image: Image.Image):
if image is None:
return pd.DataFrame(), "Please upload or take a photo."
image = image.convert("RGB")
inputs = processor(images=image, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
logits = outputs.logits
probs = torch.nn.functional.softmax(logits, dim=1)[0]
threshold = 0.40
data = []
likely_conditions = []
for idx, prob in enumerate(probs):
label = label_map[idx] # this is the exact model label
conf = prob.item()
status = "✅ Positive" if conf > threshold else "❌ Negative"
desc = condition_info.get(label, "No description available.")
data.append({
"Condition": label.replace("_", " ").capitalize(),
"Confidence (%)": f"{conf*100:.2f}",
"Status": status,
"What it means": desc
})
if conf > threshold:
likely_conditions.append(label.replace("_", " ").capitalize())
df = pd.DataFrame(data)
summary_text = (
"🧾 **Summary:** " +
(", ".join(likely_conditions) if likely_conditions else "No major concern seen by AI.") +
"\n\n📢 Please check with a real doctor for correct diagnosis."
)
return df, summary_text
# Gradio UI
demo = gr.Interface(
fn=classify_skin,
inputs=gr.Image(type="pil", label="📷 Upload or Capture Skin Image"),
outputs=[
gr.Dataframe(headers=["Condition", "Confidence (%)", "Status", "What it means"]),
gr.Markdown()
],
title="AI Skin Condition Classifier",
description="Upload a photo of a skin issue. The AI will check 7 common conditions and suggest what's likely. For support only — not a diagnosis."
)
if __name__ == "__main__":
demo.launch()
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