added camera feature

app.py

@@ -35,7 +35,7 @@ st.markdown(
 
 # Streamlit app title and instructions
 st.title("VisionDerm")
-st.write("Upload an image to identify the skin condition.")
+st.write("Upload an image or use your camera to identify the skin condition.")
 
 # Column layout for input and display
 col1, col2 = st.columns([3, 2])
@@ -43,20 +43,28 @@ col1, col2 = st.columns([3, 2])
 with col1:
     # File uploader for image
     uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+    # Camera input for photo capture
+    camera_photo = st.camera_input("Take a photo")
     prompt = 'Identify the skin condition?'
 
+# Choose input image (either uploaded or taken by camera)
+input_image = None
+if camera_photo:
+    input_image = Image.open(camera_photo)
+elif uploaded_file:
+    input_image = Image.open(uploaded_file)
+
 with col2:
-    if uploaded_file:
-        # Open and resize the uploaded image
-        input_image = Image.open(uploaded_file).convert("RGB")
+    if input_image:
+        # Display the image
         resized_image = input_image.resize((300, 300))
-        st.image(resized_image, caption="Uploaded Image (300x300)", use_container_width=True)
+        st.image(resized_image, caption="Selected Image (300x300)", use_container_width=True)
 
         # Resize image for processing (512x512 pixels)
         max_size = (512, 512)
         processed_image = input_image.resize(max_size)
 
-        # Predict automatically when the image is uploaded or the prompt changes
+        # Predict automatically when the image is uploaded or captured
         with st.spinner("Processing..."):
             try:
                 # Prepare inputs
@@ -72,21 +80,17 @@ with col2:
                 with torch.no_grad():
                     outputs = model.generate(**inputs, max_new_tokens=default_max_tokens)
 
-                # Decode output and remove the prompt text from the response
+                # Decode output and remove the prompt text
                 decoded_output = processor.decode(outputs[0], skip_special_tokens=True)
-                
-                # Remove the prompt text from the model output if present
                 if prompt in decoded_output:
                     decoded_output = decoded_output.replace(prompt, "").strip()
-                
+
                 # Capitalize the first letter of each word
                 decoded_output = decoded_output.title()
-                
+
                 # Display result
                 st.success("Analysis Complete!")
                 st.write("**Model Output:**", decoded_output)
 
-
-
             except Exception as e:
                 st.error(f"Error: {str(e)}")