Update app.py

app.py

@@ -45,7 +45,7 @@ st.markdown(input_field_style, unsafe_allow_html=True)
 
 
 # Creating Tabs
-tab1, tab2 = st.tabs(["Image Generation", "Text Generation"])
+tab1, tab2 = st.tabs(["Image Generation", "Architecture"])
 
 with tab1:
     # Create two columns for layout
@@ -123,63 +123,9 @@ with tab1:
 with tab2:        
     # ===========
     # Define Streamlit UI elements
-    st.title('Stable Diffusion XL Image Generation')
-
-
-
-    prompt = st.text_area("Enter your prompt:", 
-                        "Raccoons astronaut in space, sci-fi, future, cold color palette, muted colors, detailed, 8k")
-
-    # Number of inference steps
-    num_inference_steps = st.slider("Number of Inference Steps", 
-                                min_value=1, 
-                                max_value=100, 
-                                value=40, 
-                                help="More steps might improve quality, with diminishing marginal returns. 30-50 seems best, but your mileage may vary.")
-
-    # Create an expandable section for optional parameters
-    with st.expander("Optional Parameters"):
-        # Random seed input
-        seed = st.number_input("Random seed", 
-                            value=42, 
-                            help="Set to the same value to generate the same image if other inputs are the same, change to generate a different image for same inputs.")
-
-        # Negative prompt input
-        negative_prompt = st.text_area("Enter your negative prompt:", 
-                        "anime, cartoon, graphic, text, painting, crayon, graphite, abstract glitch, blurry")
-
-    
-
-
-
-
-
-    if st.button('Generate Image:'):
-        with st.spinner(f'Generating Image with {num_inference_steps} iterations'):
-            # ===============
-            # Example input data
-            prompt_input = {
-                "prompt": prompt,
-                "parameters": {
-                    "num_inference_steps": num_inference_steps,
-                    "seed": seed,
-                    "negative_prompt": negative_prompt
-                }
-            }
-
-            # Make API request
-            response = requests.post(api_url, json=prompt_input)
-
-            # Process and display the response
-            if response.status_code == 200:
-                result = response.json()
-                # st.success(f"Prediction result: {result}")
-                image2 = display_image(decode_base64_image(result["generated_images"][0]))
-                st.header("SDXL Base")
-                st.image(image2, 
-                    caption=f"SDXL Base, {num_inference_steps} iterations") 
-            else:
-                st.error(f"Error: {response.text}")
+    st.title('Architecture')
+    st.image('./architecture.png', caption=f"Application Architecture") 
+