Update app.py

app.py

@@ -1,141 +1,141 @@
-import gradio as gr
-import os
-from io import BytesIO
-from PIL import Image, ImageDraw, ImageFont
-from PIL import ImageColor
-import json
-import google.generativeai as genai
-from google.generativeai import types
-from dotenv import load_dotenv
-from IPython.display import display
-
-# 1. SETUP API KEY
-# ----------------
-load_dotenv()
-api_key = os.getenv("Gemini_API_Key") 
-# Configure the Google AI library
-genai.configure(api_key=api_key)
-
-
-# 2. DEFINE MODEL AND INSTRUCTIONS
-
-bounding_box_system_instructions = """
-    Return bounding boxes as a JSON array with labels. Never return masks or code fencing. Limit to 25 objects.
-    If an object is present multiple times, name them according to their unique characteristic (colors, size, position, unique characteristics, etc..).
-      """
-model = genai.GenerativeModel( model_name='gemini-2.5-flash', system_instruction=bounding_box_system_instructions)
-generation_config = genai.types.GenerationConfig(
-        temperature=0.5,
- 
-    )   
-
-
-def generate_bounding_boxes(prompt, image):
-    image = image.resize((1024, int(1024 * image.height / image.width)))
-    response = model.generate_content([prompt, image], generation_config=generation_config)
-    bounding_boxes = parse_json(response.text)
-    img=plot_bounding_boxes(image, bounding_boxes)
-    return img
-
-
-def parse_json(json_output): 
-    lines = json_output.splitlines()
-    for i, line in enumerate(lines):
-        if line == "```json":
-            json_output = "\n".join(lines[i+1:])  # Remove everything before "```json"
-            json_output = json_output.split("```")[0]  # Remove everything after the closing "```"
-            break
-    return json_output
-
-def plot_bounding_boxes(im, bounding_boxes):
-    """
-    Plots bounding boxes on an image with labels.
-    """
-    additional_colors = [colorname for (colorname, colorcode) in ImageColor.colormap.items()]
-
-    im = im.copy()
-    width, height = im.size
-    draw = ImageDraw.Draw(im)
-    colors = [
-        'red', 'green', 'blue', 'yellow', 'orange', 'pink', 'purple', 'cyan',
-        'lime', 'magenta', 'violet', 'gold', 'silver'
-    ] + additional_colors
-
-    try:
-        # Use a default font if NotoSansCJK is not available
-        try:
-            font = ImageFont.load_default()
-        except OSError:
-            print("NotoSansCJK-Regular.ttc not found. Using default font.")
-            font = ImageFont.load_default()
-
-        bounding_boxes_json = json.loads(bounding_boxes)
-        for i, bounding_box in enumerate(bounding_boxes_json):
-            color = colors[i % len(colors)]
-            abs_y1 = int(bounding_box["box_2d"][0] / 1000 * height)
-            abs_x1 = int(bounding_box["box_2d"][1] / 1000 * width)
-            abs_y2 = int(bounding_box["box_2d"][2] / 1000 * height)
-            abs_x2 = int(bounding_box["box_2d"][3] / 1000 * width)
-
-            if abs_x1 > abs_x2:
-                abs_x1, abs_x2 = abs_x2, abs_x1
-
-            if abs_y1 > abs_y2:
-                abs_y1, abs_y2 = abs_y2, abs_y1
-
-            # Draw bounding box and label
-            draw.rectangle(((abs_x1, abs_y1), (abs_x2, abs_y2)), outline=color, width=4)
-            if "label" in bounding_box:
-                draw.text((abs_x1 + 8, abs_y1 + 6), bounding_box["label"], fill=color, font=font)
-    except Exception as e:
-        print(f"Error drawing bounding boxes: {e}")
-
-    return im
-def gradio_interface():
-    """
-    Gradio app interface for bounding box generation with example pairs.
-    """
-    # Example image + prompt pairs
-    examples = [
-        ["cookies.jpg", "Detect the cookies and label their types."],
-        ["messed_room.jpg", "Find the unorganized item and suggest action in label in the image to fix them."],
-        ["yoga.jpg", "Show the different yoga poses and name them."],
-        ["zoom_face.png", "Label the tired faces in the image."]
-    ]
-
-    with gr.Blocks(gr.themes.Glass(secondary_hue= "rose")) as demo:
-        gr.Markdown("# Gemini Bounding Box Generator")
-
-        with gr.Row():
-            with gr.Column():
-                gr.Markdown("### Input Section")
-                input_image = gr.Image(type="pil", label="Input Image")
-                input_prompt = gr.Textbox(lines=2, label="Input Prompt", placeholder="Describe what to detect.")
-                submit_btn = gr.Button("Generate")
-
-            with gr.Column():
-                gr.Markdown("### Output Section")
-                output_image = gr.Image(type="pil", label="Output Image")
-                #output_json = gr.Textbox(label="Bounding Boxes JSON")
-
-        gr.Markdown("### Examples")
-        gr.Examples(
-            examples=examples,
-            inputs=[input_image, input_prompt],
-            label="Example Images with Prompts"
-        )
-
-        # Event to generate bounding boxes
-        submit_btn.click(
-            generate_bounding_boxes,
-            inputs=[input_prompt, input_image],
-            outputs=[output_image]
-        )
-
-    return demo
-
-
-
-if __name__ == "__main__":
-    app = gradio_interface()
+import gradio as gr
+import os
+from io import BytesIO
+from PIL import Image, ImageDraw, ImageFont
+from PIL import ImageColor
+import json
+import google.generativeai as genai
+from google.generativeai import types
+from dotenv import load_dotenv
+
+
+# 1. SETUP API KEY
+# ----------------
+load_dotenv()
+api_key = os.getenv("Gemini_API_Key") 
+# Configure the Google AI library
+genai.configure(api_key=api_key)
+
+
+# 2. DEFINE MODEL AND INSTRUCTIONS
+
+bounding_box_system_instructions = """
+    Return bounding boxes as a JSON array with labels. Never return masks or code fencing. Limit to 25 objects.
+    If an object is present multiple times, name them according to their unique characteristic (colors, size, position, unique characteristics, etc..).
+      """
+model = genai.GenerativeModel( model_name='gemini-2.5-flash', system_instruction=bounding_box_system_instructions)
+generation_config = genai.types.GenerationConfig(
+        temperature=0.5,
+ 
+    )   
+
+
+def generate_bounding_boxes(prompt, image):
+    image = image.resize((1024, int(1024 * image.height / image.width)))
+    response = model.generate_content([prompt, image], generation_config=generation_config)
+    bounding_boxes = parse_json(response.text)
+    img=plot_bounding_boxes(image, bounding_boxes)
+    return img
+
+
+def parse_json(json_output): 
+    lines = json_output.splitlines()
+    for i, line in enumerate(lines):
+        if line == "```json":
+            json_output = "\n".join(lines[i+1:])  # Remove everything before "```json"
+            json_output = json_output.split("```")[0]  # Remove everything after the closing "```"
+            break
+    return json_output
+
+def plot_bounding_boxes(im, bounding_boxes):
+    """
+    Plots bounding boxes on an image with labels.
+    """
+    additional_colors = [colorname for (colorname, colorcode) in ImageColor.colormap.items()]
+
+    im = im.copy()
+    width, height = im.size
+    draw = ImageDraw.Draw(im)
+    colors = [
+        'red', 'green', 'blue', 'yellow', 'orange', 'pink', 'purple', 'cyan',
+        'lime', 'magenta', 'violet', 'gold', 'silver'
+    ] + additional_colors
+
+    try:
+        # Use a default font if NotoSansCJK is not available
+        try:
+            font = ImageFont.load_default()
+        except OSError:
+            print("NotoSansCJK-Regular.ttc not found. Using default font.")
+            font = ImageFont.load_default()
+
+        bounding_boxes_json = json.loads(bounding_boxes)
+        for i, bounding_box in enumerate(bounding_boxes_json):
+            color = colors[i % len(colors)]
+            abs_y1 = int(bounding_box["box_2d"][0] / 1000 * height)
+            abs_x1 = int(bounding_box["box_2d"][1] / 1000 * width)
+            abs_y2 = int(bounding_box["box_2d"][2] / 1000 * height)
+            abs_x2 = int(bounding_box["box_2d"][3] / 1000 * width)
+
+            if abs_x1 > abs_x2:
+                abs_x1, abs_x2 = abs_x2, abs_x1
+
+            if abs_y1 > abs_y2:
+                abs_y1, abs_y2 = abs_y2, abs_y1
+
+            # Draw bounding box and label
+            draw.rectangle(((abs_x1, abs_y1), (abs_x2, abs_y2)), outline=color, width=4)
+            if "label" in bounding_box:
+                draw.text((abs_x1 + 8, abs_y1 + 6), bounding_box["label"], fill=color, font=font)
+    except Exception as e:
+        print(f"Error drawing bounding boxes: {e}")
+
+    return im
+def gradio_interface():
+    """
+    Gradio app interface for bounding box generation with example pairs.
+    """
+    # Example image + prompt pairs
+    examples = [
+        ["cookies.jpg", "Detect the cookies and label their types."],
+        ["messed_room.jpg", "Find the unorganized item and suggest action in label in the image to fix them."],
+        ["yoga.jpg", "Show the different yoga poses and name them."],
+        ["zoom_face.png", "Label the tired faces in the image."]
+    ]
+
+    with gr.Blocks(gr.themes.Glass(secondary_hue= "rose")) as demo:
+        gr.Markdown("# Gemini Bounding Box Generator")
+
+        with gr.Row():
+            with gr.Column():
+                gr.Markdown("### Input Section")
+                input_image = gr.Image(type="pil", label="Input Image")
+                input_prompt = gr.Textbox(lines=2, label="Input Prompt", placeholder="Describe what to detect.")
+                submit_btn = gr.Button("Generate")
+
+            with gr.Column():
+                gr.Markdown("### Output Section")
+                output_image = gr.Image(type="pil", label="Output Image")
+                #output_json = gr.Textbox(label="Bounding Boxes JSON")
+
+        gr.Markdown("### Examples")
+        gr.Examples(
+            examples=examples,
+            inputs=[input_image, input_prompt],
+            label="Example Images with Prompts"
+        )
+
+        # Event to generate bounding boxes
+        submit_btn.click(
+            generate_bounding_boxes,
+            inputs=[input_prompt, input_image],
+            outputs=[output_image]
+        )
+
+    return demo
+
+
+
+if __name__ == "__main__":
+    app = gradio_interface()
     app.launch()