Update app.py

app.py

@@ -17,44 +17,51 @@ model.to(device)
 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"])
+# Column layout for input and display
+col1, col2 = st.columns([3, 2])
 
-# Text input for prompt
-input_text = st.text_input("Enter your prompt:", "Identify the skin condition?")
-
-# Slider for max tokens
-max_new_tokens = st.slider("Maximum Output Tokens:", 10, 100, 50)
-
-# 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
+with col1:
+    # File uploader for image
+    uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+    
+    # Display uploaded image (if any)
+    if uploaded_file:
         input_image = Image.open(uploaded_file).convert("RGB")
         st.image(input_image, caption="Uploaded Image", use_column_width=True)
+    
+    # Text input for prompt
+    input_text = st.text_input("Enter your prompt:", "Identify the skin condition?")
 
-        # Resize image for efficiency
-        max_size = (512, 512)
-        input_image = input_image.resize(max_size)
-
-        # Prepare inputs
-        with st.spinner("Processing..."):
-            inputs = processor(
-                text=input_text,
-                images=input_image,
-                return_tensors="pt",
-                padding="longest"
-            ).to(device)
-
-            # Generate output
-            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)}")
+with col2:
+    # Process and display the result when the button is clicked
+    if uploaded_file and st.button("Analyze"):
+        if not input_text.strip():
+            st.error("Please provide a valid prompt!")
+        else:
+            try:
+                # Resize image for efficiency
+                max_size = (512, 512)
+                input_image = input_image.resize(max_size)
+    
+                # Prepare inputs
+                with st.spinner("Processing..."):
+                    inputs = processor(
+                        text=input_text,
+                        images=input_image,
+                        return_tensors="pt",
+                        padding="longest"
+                    ).to(device)
+    
+                    # Generate output with default max_new_tokens
+                    default_max_tokens = 50  # Set a default value for max tokens
+                    with torch.no_grad():
+                        outputs = model.generate(**inputs, max_new_tokens=default_max_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)}")