update: answer workshop genai

image_examples/image_gen_st.py

@@ -112,3 +112,38 @@ def generate_image_titan(text):
 
 
 model = st.selectbox("Select model", ["Amazon Titan", "Stable Diffusion"])
+prompt = st.text_input("Enter prompt")
+# List of Stable Diffusion Preset Styles
+sd_presets = [
+    "None", "3d-model", "analog-film", "anime", "cinematic", "comic-book",
+    "digital-art", "enhance", "fantasy-art", "isometric", "line-art",
+    "low-poly", "modeling-compound", "neon-punk", "origami", "photographic",
+    "pixel-art", "tile-texture"
+]
+
+# Select box for styles (only if Stable Diffusion is selected)
+if model == "Stable Diffusion":
+    style = st.selectbox("Select Style", sd_presets)
+else:
+    style = "None"
+import base64
+from PIL import Image
+from io import BytesIO
+
+def base64_to_pil(base64_str):
+    """
+    Converts a base64 string to a PIL Image object.
+    """
+    image_data = base64.b64decode(base64_str)
+    image = Image.open(BytesIO(image_data))
+    return image
+if st.button("Generate Image"):
+    if model == "Amazon Titan":
+        image_base64 = generate_image_titan(prompt)
+    else:
+        image_base64 = generate_image_sd(prompt, style)
+    # Convert base64 to PIL Image
+    image = base64_to_pil(image_base64)
+
+    # Display the image
+    st.image(image)

image_examples/image_to_image_st.py

@@ -50,7 +50,6 @@ def base64_to_pil(base64_string):
     return image
 
 
-# Bedrock api call to stable diffusion
 def sd_update_image(change_prompt, init_image_b64):
     """
     Purpose:
@@ -143,15 +142,22 @@ def update_image_pipeline(user_image, change_prompt, model):
 
 st.title("Building with Bedrock")  # Title of the application
 st.subheader("Image Generation Demo - Image to Image")
+model = st.selectbox(
+    "Select model",
+    [
+        "Amazon Titan",
+        "Stable Diffusion",
+    ],
+)
 
-model = st.selectbox("Select model", ["Amazon Titan", "Stable Diffusion"])
-
-# TODO insert your comments
 
+user_image = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])
+change_prompt = st.text_input("Enter prompt to change image")
 
-col1, col2 = st.columns(2)  # Column 1 for input image, Column 2 for output image
+# Creat two columns, one to show the uploaded image, another for the new image
+col1, col2 = st.columns(2)
 
-# show user image
+# Show the uploaded image
 if user_image is not None:
     user_image = Image.open(user_image)
     col1.image(user_image)

image_examples/image_understanding_st.py

@@ -73,6 +73,12 @@ def pil_to_base64(image, format="png"):
 if user_image is not None:
     user_image = Image.open(user_image)
     # TODO Finish App with Q
+    col1.image(user_image, use_column_width=True)
+
+    if col1.button("Get caption"):
+        base64_image = pil_to_base64(user_image)
+        caption = call_claude_sonnet(base64_image)
+        col2.write(caption)
 
 else:
     col2.write("No image uploaded")

image_examples/inpainting_st.py

@@ -44,6 +44,7 @@ def gen_mask_from_image(user_image):
     return mask
 
 
+
 def image_to_base64(img) -> str:
     """Convert a PIL Image or local image file path to a base64 string for Amazon Bedrock"""
     if isinstance(img, str):
@@ -188,14 +189,20 @@ def inpaint_image_pipeline(user_image, change_prompt, mask, model):
 # Create three columns, one to show the uploaded image, 2 for the mask, and 3 for the new image
 col1, col2, col3 = st.columns(3)
 
-# Show the uploaded image
 if user_image is not None:
     user_image = Image.open(user_image)
     col1.image(user_image)
 
     if model == "Stable Diffusion":
-        mask = Image.open("sd_mask.png") 
+        mask = Image.open("sd_mask.png")
     else:
         mask = gen_mask_from_image(user_image)
-
-    # TODO Finish App with Q
+
+    col2.image(mask)
+    if st.button("Update Image"):
+        update_image = inpaint_image_pipeline(user_image, change_prompt, mask, model)
+        col3.image(update_image)
+else:
+    col3.write("Please upload an image to get started")
+
+

text_examples/gen_text.py

@@ -71,16 +71,15 @@ def summarize_text(text):
     """
 
     model_id = "meta.llama3-70b-instruct-v1:0"
-    # Setup the system prompts and messages to send to the model.
     system_prompts = [
         {"text": "You are an app that creates summaries of text in 50 words or less."}
     ]
-    message_1 = {
+    messages = {
         "role": "user",
         "content": [{"text": f"Summarize the following text: {text}."}],
     }
 
-    messages = [message_1]
+    messages = [messages]
 
     result = generate_conversation(model_id, system_prompts, messages)
 
@@ -92,7 +91,18 @@ def sentiment_analysis(text):
     Function to return a JSON object of sentiment from a given text.
     """
     # TODO Can you fill in the function?
-    result = None
+    model_id = "anthropic.claude-3-sonnet-20240229-v1:0"
+    system_prompts = [
+        {
+            "text": "You are a sentiment analysis bot that returns a JSON object of sentiment from a given text."
+        }
+    ]
+    messages = [{
+        "role": "user",
+        "content": [{"text": f"Analyze the sentiment of the following text: {text}."}],
+    }]
+    result = generate_conversation(model_id, system_prompts, messages)
+    # result = None
     return result
 
 
@@ -101,7 +111,20 @@ def perform_qa(question, text):
     Function to perform a Q&A operation based on the provided text.
     """
     # TODO Can you fill in the function?
-    result = None
+    model_id = "mistral.mistral-large-2402-v1:0"
+    system_prompts = [
+        {
+            "text": f"Please you following this text: {text}."
+        }
+    ]
+    messages = [{
+        "role": "user",
+        "content": [
+            {"text": f"Question: {question}"}
+        ],
+    }]
+    result = generate_conversation(model_id, system_prompts, messages)
+    # result = None
     return result