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.env

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+Gemini_API_Key="AIzaSyDi3ZnyT06gbVpRAtgk49yGNj4gxcMPLdA"

app.py

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+import gradio as gr
+import os
+import json
+import google.generativeai as genai
+from PIL import Image, ImageDraw, ImageFont, ImageColor
+from dotenv import load_dotenv
+
+load_dotenv()
+api_key = os.getenv("Gemini_API_Key") 
+genai.configure(api_key=api_key)
+
+SYSTEM_INSTRUCTION = """
+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.
+Use the format: [{"box_2d": [ymin, xmin, ymax, xmax], "label": "object_name"}]
+"""
+
+model = genai.GenerativeModel(
+    model_name='gemini-2.5-flash', 
+    system_instruction=SYSTEM_INSTRUCTION
+)
+
+def parse_response(text):
+    """Cleans markdown formatting if the model includes it."""
+    clean = text.replace("json", "").replace("", "").strip()
+    return clean
+
+def draw_boxes(image, boxes_json):
+    """Draws boxes on the image using normalized coordinates (0-1000)."""
+    img = image.copy()
+    draw = ImageDraw.Draw(img)
+    width, height = img.size
+    colors = ["red", "green", "blue", "yellow", "magenta", "cyan", "orange"]
+
+    try:
+        data = json.loads(boxes_json)
+        for i, item in enumerate(data):
+            color = colors[i % len(colors)]
+            # Gemini order: [ymin, xmin, ymax, xmax]
+            ymin, xmin, ymax, xmax = item["box_2d"]
+            
+            left = (xmin / 1000) * width
+            top = (ymin / 1000) * height
+            right = (xmax / 1000) * width
+            bottom = (ymax / 1000) * height
+            
+            draw.rectangle([left, top, right, bottom], outline=color, width=5)
+            draw.text((left + 5, top + 5), item.get("label", "Object"), fill=color)
+    except Exception as e:
+        print(f"Error drawing boxes: {e}")
+    
+    return img
+
+def process_image(input_img, user_instruction):
+    if input_img is None:
+        return None
+    
+    if not user_instruction.strip():
+        user_instruction = "Identify and label the objects in the image."
+
+    # Combine user instruction with JSON formatting requirement
+    full_prompt = f"{user_instruction} Return ONLY the JSON array of bounding boxes."
+    
+    response = model.generate_content([full_prompt, input_img])
+    json_data = parse_response(response.text)
+    
+    output_img = draw_boxes(input_img, json_data)
+    return output_img
+
+demo = gr.Interface(
+    fn=process_image,
+    inputs=[
+        gr.Image(type="pil", label="Upload Image"),
+        gr.Textbox(
+            label="What should I find?", 
+            placeholder="e.g., 'Find all the red apples' or 'Detect the cars and pedestrians'",
+            lines=2
+        )
+    ],
+    outputs=gr.Image(type="pil", label="Image with Bounding Boxes"),
+    title="Dynamic Gemini Object Detector",
+    description="Upload an image and give specific instructions on what to detect."
+)
+
+if __name__ == "__main__":
+    demo.launch()