Create app.py

app.py

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+# app.py
+
+import streamlit as st
+from PIL import Image
+import torch
+from transformers import AutoImageProcessor, AutoModelForImageClassification
+
+# App title and instructions
+st.set_page_config(page_title="Skin Condition Classifier", layout="centered")
+st.title("🧠 AI Skin Condition Classifier")
+st.markdown("Upload a **clear photo** of the skin condition to receive AI-powered predictions.")
+
+# Image uploader
+uploaded_file = st.file_uploader("📷 Upload a skin image", type=["jpg", "jpeg", "png"])
+
+# Load the pre-trained model
+@st.cache_resource
+def load_model():
+    model_name = "Anwarkh1/Skin_Cancer-Image_Classification"
+    processor = AutoImageProcessor.from_pretrained(model_name)
+    model = AutoModelForImageClassification.from_pretrained(model_name)
+    return processor, model
+
+processor, model = load_model()
+
+# Handle image upload and prediction
+if uploaded_file is not None:
+    image = Image.open(uploaded_file).convert("RGB")
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    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]
+
+    # Top 3 predictions
+    top_probs, top_indices = torch.topk(probs, k=3)
+    class_labels = model.config.id2label
+
+    st.subheader("🧾 Prediction Results")
+    for idx, prob in zip(top_indices, top_probs):
+        label = class_labels[idx.item()]
+        st.write(f"**{label}** – {prob.item() * 100:.2f}%")
+
+    st.info("🔍 Note: This tool is for supportive use only. Please consult a dermatologist for a medical diagnosis.")