Create app.py

app.py

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+import streamlit as st
+from transformers import AutoModelForImageClassification, AutoImageProcessor
+from PIL import Image
+import torch
+
+# Set the title of the application
+st.title("Dermavision")
+st.write(
+    "Upload an image of the affected skin area, and the app will classify the disease and provide analysis."
+)
+
+# Cache model and processor loading
+@st.cache_resource
+def load_model():
+    repo_name = "Jayanth2002/dinov2-base-finetuned-SkinDisease"
+    processor = AutoImageProcessor.from_pretrained(repo_name)
+    model = AutoModelForImageClassification.from_pretrained(repo_name)
+    return model, processor
+
+model, processor = load_model()
+
+# Define the class names
+class_names = [
+    'Basal Cell Carcinoma', 'Darier_s Disease', 'Epidermolysis Bullosa Pruriginosa',
+    'Hailey-Hailey Disease', 'Herpes Simplex', 'Impetigo', 'Larva Migrans',
+    'Leprosy Borderline', 'Leprosy Lepromatous', 'Leprosy Tuberculoid', 'Lichen Planus',
+    'Lupus Erythematosus Chronicus Discoides', 'Melanoma', 'Molluscum Contagiosum',
+    'Mycosis Fungoides', 'Neurofibromatosis', 'Papilomatosis Confluentes And Reticulate',
+    'Pediculosis Capitis', 'Pityriasis Rosea', 'Porokeratosis Actinic', 'Psoriasis',
+    'Tinea Corporis', 'Tinea Nigra', 'Tungiasis', 'actinic keratosis', 'dermatofibroma',
+    'nevus', 'pigmented benign keratosis', 'seborrheic keratosis', 'squamous cell carcinoma',
+    'vascular lesion'
+]
+
+# Define reasons, treatments, and home remedies for each disease
+# (This section is omitted for brevity but should remain unchanged from your original code)
+
+# Function to classify the image
+def classify_image(image):
+    inputs = processor(image.convert("RGB"), return_tensors="pt")
+    with torch.no_grad():
+        outputs = model(**inputs)
+        predicted_class_idx = outputs.logits.argmax(-1).item()
+        confidence_score = torch.nn.functional.softmax(outputs.logits, dim=-1).max().item()
+    predicted_label = class_names[predicted_class_idx]
+    return predicted_label, confidence_score
+
+# File uploader for user image
+uploaded_file = st.file_uploader("Upload a skin image", type=["jpg", "jpeg", "png"])
+
+if uploaded_file is not None:
+    # Display the uploaded image
+    image = Image.open(uploaded_file)
+    st.image(image, caption="Uploaded Image", use_column_width=True)
+
+    # Analyze the image
+    with st.spinner("Analyzing the image..."):
+        predicted_label, confidence_score = classify_image(image)
+        if predicted_label not in disease_analysis:
+            st.error("Unable to classify the disease. Please upload a clearer image or consult a dermatologist.")
+        else:
+            reason = disease_analysis.get(predicted_label, {}).get("reason", "Reason unknown.")
+            treatment = disease_analysis.get(predicted_label, {}).get("treatment", "Consult a dermatologist.")
+            home_remedy = disease_analysis.get(predicted_label, {}).get("home_remedy", "No specific home remedies available.")
+
+            # Display the results
+            st.success("Analysis Complete!")
+            st.markdown(f"### **Classification**: {predicted_label}")
+            st.markdown(f"**Confidence**: {confidence_score:.2%}")
+            st.markdown(f"**Reason**: {reason}")
+            st.markdown(f"**Treatment**: {treatment}")
+            st.markdown(f"**Home Remedy**: {home_remedy}")
+            st.markdown("**Note:** Please consult a doctor for final recommendations and a detailed treatment plan.")
+
+# Optional feedback form
+st.markdown("---")
+st.header("We Value Your Feedback!")
+feedback = st.text_area("Please share your feedback to help us improve:")
+if st.button("Submit Feedback"):
+    if feedback:
+        st.success("Thank you for your feedback!")
+    else:
+        st.warning("Feedback cannot be empty.")