Create app.py

app.py

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+import streamlit as st
+import torch
+from transformers import AutoProcessor, PaliGemmaForConditionalGeneration
+from PIL import Image
+
+# Load the model and processor
+model_id = "brucewayne0459/paligemma_derm"
+processor = AutoProcessor.from_pretrained(model_id)
+model = PaliGemmaForConditionalGeneration.from_pretrained(model_id, device_map={"": 0})
+model.eval()
+
+# Set device
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model.to(device)
+
+# Streamlit app
+st.title("Skin Condition Identifier")
+st.write("Upload an image and provide a text prompt to identify the skin condition.")
+
+# File uploader for image
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+
+# Text input for prompt
+input_text = st.text_input("Enter your prompt:", "Identify the skin condition?")
+
+# Process and display the result when the button is clicked
+if uploaded_file is not None and st.button("Analyze"):
+    try:
+        # Open the uploaded image
+        input_image = Image.open(uploaded_file).convert("RGB")
+        st.image(input_image, caption="Uploaded Image", use_column_width=True)
+
+        # Prepare inputs
+        inputs = processor(
+            text=input_text,
+            images=input_image,
+            return_tensors="pt",
+            padding="longest"
+        ).to(device)
+
+        # Generate output
+        max_new_tokens = 50
+        with torch.no_grad():
+            outputs = model.generate(**inputs, max_new_tokens=max_new_tokens)
+        
+        # Decode output
+        decoded_output = processor.decode(outputs[0], skip_special_tokens=True)
+
+        # Display result
+        st.success("Analysis Complete!")
+        st.write("**Model Output:**", decoded_output)
+    except Exception as e:
+        st.error(f"Error: {str(e)}")