Update app.py

app.py

@@ -1,139 +1,139 @@
-import google.generativeai as genai
-from google.generativeai.types import HarmBlockThreshold, HarmCategory
-import gradio as gr
-from PIL import Image, ImageDraw, ImageFont
-import json
-
-# Fetch bounding boxes and labels
-async def get_bounding_boxes(prompt: str, image: str, api_key: str):
-    system_prompt = """
-You are a helpful assistant, who always responds with the bounding box and label with the explanation JSON based on the user input, and nothing else.
-Your response can also include multiple bounding boxes and their labels in the list.
-The values in the list should be integers.
-Here are some example responses:
-{
-    "explanation": "User asked for the bounding box of the dragon, so I will provide the bounding box of the dragon.",
-    "bounding_boxes": [
-        {"label": "dragon", "box": [ymin, xmin, ymax, xmax]}
-    ]
-}
-{
-    "explanation": "User asked for the bounding box of the fruits which are red in color, so I will provide the bounding box of the Apple and the Tomato.",
-    "bounding_boxes": [
-        {"label": "apple", "box": [ymin, xmin, ymax, xmax]},
-        {"label": "tomato", "box": [ymin, xmin, ymax, xmax]}
-    ]
-}
-""".strip()
-    
-    prompt = f"Return the bounding boxes and labels of: {prompt}"
-
-    messages = [
-        {"role": "user", "parts": [prompt, image]},
-    ]
-
-    genai.configure(api_key=api_key)
-
-    generation_config = {
-        "temperature": 1,
-        "max_output_tokens": 8192,
-        "response_mime_type": "application/json",
-    }
-
-    model = genai.GenerativeModel(
-        model_name="gemini-1.5-flash",
-        generation_config=generation_config,
-        safety_settings={
-            HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
-            HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
-            HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
-            HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE
-        },
-        system_instruction=system_prompt
-    )
-
-    try:
-        response = await model.generate_content_async(messages)
-    except Exception as e:
-        if "API key not valid" in str(e):
-            raise gr.Error(
-                "Invalid API key. Please provide a valid Gemini API key.")
-        elif "rate limit" in str(e).lower():
-            raise gr.Error("Rate limit exceeded for the API key.")
-        else:
-            raise gr.Error(f"Failed to generate content: {str(e)}")
-
-    response_json = json.loads(response.text)
-
-    explanation = response_json["explanation"]
-    bounding_boxes = response_json["bounding_boxes"]
-
-    return bounding_boxes, explanation
-
-# Adjust bounding boxes based on image size
-async def adjust_bounding_box(bounding_boxes, image):
-    width, height = image.size
-    adjusted_boxes = []
-    for item in bounding_boxes:
-        label = item["label"]
-        ymin, xmin, ymax, xmax = [coord / 1000 for coord in item["box"]]
-        xmin *= width
-        xmax *= width
-        ymin *= height
-        ymax *= height
-        adjusted_boxes.append({"label": label, "box": [xmin, ymin, xmax, ymax]})
-    return adjusted_boxes
-
-# Process the image and draw bounding boxes and labels
-async def process_image(image, text, api_key):
-    if not api_key:
-        raise gr.Error("Please provide a Gemini API key.")
-
-    # Open the image using PIL
-    image = Image.open(image)
-
-    # Call the async bounding box function
-    bounding_boxes, explanation = await get_bounding_boxes(text, image, api_key)
-
-    # Adjust the bounding box based on the image dimensions
-    adjusted_boxes = await adjust_bounding_box(bounding_boxes, image)
-
-    # Draw the bounding boxes and labels on the image
-    draw = ImageDraw.Draw(image)
-    font = ImageFont.load_default(size=20)
-    
-    for item in adjusted_boxes:
-        box = item["box"]
-        label = item["label"]
-        draw.rectangle(box, outline="red", width=3)
-        # Draw the label above the bounding box
-        draw.text((box[0], box[1] - 25), label, fill="red", font=font)
-
-    # Format adjusted boxes for display
-    adjusted_boxes_str = "\n".join(f"{item['label']}: {item['box']}" for item in adjusted_boxes)
-
-    return explanation, image, adjusted_boxes_str
-
-# Gradio app
-async def gradio_app(image, text, api_key):
-    return await process_image(image, text, api_key)
-
-# Launch the Gradio interface
-iface = gr.Interface(
-    fn=gradio_app,
-    inputs=[
-        gr.Image(type="filepath"),
-        gr.Textbox(label="Object(s) to detect", value="person"),
-        gr.Textbox(label="Your Gemini API Key", type="password")
-    ],
-    outputs=[
-        gr.Textbox(label="Explanation"),
-        gr.Image(type="pil", label="Output Image"),
-        gr.Textbox(label="Coordinates and Labels of the Bounding Box(es)")
-    ],
-    title="Gemini Object Detection ✨",
-    description="Detect objects in images using the Gemini 1.5 Flash model.",
-    allow_flagging="never"
-)
-
-iface.launch()
+import google.generativeai as genai
+from google.generativeai.types import HarmBlockThreshold, HarmCategory
+import gradio as gr
+from PIL import Image, ImageDraw, ImageFont
+import json
+
+# Fetch bounding boxes and labels
+async def get_bounding_boxes(prompt: str, image: str, api_key: str):
+    system_prompt = """
+You are a helpful assistant, who always responds with the bounding box and label with the explanation JSON based on the user input, and nothing else.
+Your response can also include multiple bounding boxes and their labels in the list.
+The values in the list should be integers.
+Here are some example responses:
+{
+    "explanation": "User asked for the bounding box of the dragon, so I will provide the bounding box of the dragon.",
+    "bounding_boxes": [
+        {"label": "dragon", "box": [ymin, xmin, ymax, xmax]}
+    ]
+}
+{
+    "explanation": "User asked for the bounding box of the fruits which are red in color, so I will provide the bounding box of the Apple and the Tomato.",
+    "bounding_boxes": [
+        {"label": "apple", "box": [ymin, xmin, ymax, xmax]},
+        {"label": "tomato", "box": [ymin, xmin, ymax, xmax]}
+    ]
+}
+""".strip()
+    
+    prompt = f"Return the bounding boxes and labels of: {prompt}"
+
+    messages = [
+        {"role": "user", "parts": [prompt, image]},
+    ]
+
+    genai.configure(api_key=api_key)
+
+    generation_config = {
+        "temperature": 1,
+        "max_output_tokens": 8192,
+        "response_mime_type": "application/json",
+    }
+
+    model = genai.GenerativeModel(
+        model_name="gemini-1.5-flash",
+        generation_config=generation_config,
+        safety_settings={
+            HarmCategory.HARM_CATEGORY_DANGEROUS_CONTENT: HarmBlockThreshold.BLOCK_NONE,
+            HarmCategory.HARM_CATEGORY_HARASSMENT: HarmBlockThreshold.BLOCK_NONE,
+            HarmCategory.HARM_CATEGORY_SEXUALLY_EXPLICIT: HarmBlockThreshold.BLOCK_NONE,
+            HarmCategory.HARM_CATEGORY_HATE_SPEECH: HarmBlockThreshold.BLOCK_NONE
+        },
+        system_instruction=system_prompt
+    )
+
+    try:
+        response = await model.generate_content_async(messages)
+    except Exception as e:
+        if "API key not valid" in str(e):
+            raise gr.Error(
+                "Invalid API key. Please provide a valid Gemini API key.")
+        elif "rate limit" in str(e).lower():
+            raise gr.Error("Rate limit exceeded for the API key.")
+        else:
+            raise gr.Error(f"Failed to generate content: {str(e)}")
+
+    response_json = json.loads(response.text)
+
+    explanation = response_json["explanation"]
+    bounding_boxes = response_json["bounding_boxes"]
+
+    return bounding_boxes, explanation
+
+# Adjust bounding boxes based on image size
+async def adjust_bounding_box(bounding_boxes, image):
+    width, height = image.size
+    adjusted_boxes = []
+    for item in bounding_boxes:
+        label = item["label"]
+        ymin, xmin, ymax, xmax = [coord / 1000 for coord in item["box"]]
+        xmin *= width
+        xmax *= width
+        ymin *= height
+        ymax *= height
+        adjusted_boxes.append({"label": label, "box": [xmin, ymin, xmax, ymax]})
+    return adjusted_boxes
+
+# Process the image and draw bounding boxes and labels
+async def process_image(image, text, api_key):
+    if not api_key:
+        raise gr.Error("Please provide a Gemini API key.")
+
+    # Open the image using PIL
+    image = Image.open(image)
+
+    # Call the async bounding box function
+    bounding_boxes, explanation = await get_bounding_boxes(text, image, api_key)
+
+    # Adjust the bounding box based on the image dimensions
+    adjusted_boxes = await adjust_bounding_box(bounding_boxes, image)
+
+    # Draw the bounding boxes and labels on the image
+    draw = ImageDraw.Draw(image)
+    font = ImageFont.load_default(size=20)
+    
+    for item in adjusted_boxes:
+        box = item["box"]
+        label = item["label"]
+        draw.rectangle(box, outline="red", width=3)
+        # Draw the label above the bounding box
+        draw.text((box[0], box[1] - 25), label, fill="red", font=font)
+
+    # Format adjusted boxes for display
+    adjusted_boxes_str = "\n".join(f"{item['label']}: {item['box']}" for item in adjusted_boxes)
+
+    return explanation, image, adjusted_boxes_str
+
+# Gradio app
+async def gradio_app(image, text, api_key):
+    return await process_image(image, text, api_key)
+
+# Launch the Gradio interface
+iface = gr.Interface(
+    fn=gradio_app,
+    inputs=[
+        gr.Image(type="filepath"),
+        gr.Textbox(label="Object(s) to detect", value="person"),
+        gr.Textbox(label="Your Gemini API Key", type="password")
+    ],
+    outputs=[
+        gr.Textbox(label="Explanation"),
+        gr.Image(type="pil", label="Output Image"),
+        gr.Textbox(label="Coordinates of the detected objects")
+    ],
+    title="Gemini Object Detection ✨",
+    description="Detect objects in images using the Gemini 1.5 Flash model.",
+    allow_flagging="never"
+)
+
+iface.launch()