Update app.py

app.py

@@ -89,43 +89,76 @@
 
 # demo.launch()
 
-import gradio as gr
+# import gradio as gr
+
+
+# def calculator(num1, operation, num2):
+#     if operation == "add":
+#         return num1 + num2
+#     elif operation == "subtract":
+#         return num1 - num2
+#     elif operation == "multiply":
+#         return num1 * num2
+#     elif operation == "divide":
+#         return num1 / num2
+
 
+# iface = gr.Interface(
+#     calculator,
+#     ["number", gr.Radio(["add", "subtract", "multiply", "divide"]), "number"],
+#     "number",
+#     allow_flagging="manual",
+#     flagging_options=["correct", "wrong"]
+# )
 
-def calculator(num1, operation, num2):
-    if operation == "add":
-        return num1 + num2
-    elif operation == "subtract":
-        return num1 - num2
-    elif operation == "multiply":
-        return num1 * num2
-    elif operation == "divide":
-        return num1 / num2
+# iface.launch()
 
+# import os
 
-iface = gr.Interface(
-    calculator,
-    ["number", gr.Radio(["add", "subtract", "multiply", "divide"]), "number"],
-    "number",
-    allow_flagging="manual",
-    flagging_options=["correct", "wrong"]
-)
+# HF_TOKEN = os.getenv('HF_TOKEN')
+# hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "crowdsourced-calculator-demo")
 
-iface.launch()
+# iface = gr.Interface(
+#     calculator,
+#     ["number", gr.Radio(["add", "subtract", "multiply", "divide"]), "number"],
+#     "number",
+#     description="Check out the crowd-sourced dataset at: [https://huggingface.co/Sajjo/crowdsourced-calculator-demo](https://huggingface.co/Sajjo/crowdsourced-calculator-demo)",
+#     allow_flagging="manual",
+#     flagging_options=["wrong sign", "off by one", "other"],
+#     flagging_callback=hf_writer
+# )
 
-import os
+# iface.launch()
 
-HF_TOKEN = os.getenv('HF_TOKEN')
-hf_writer = gr.HuggingFaceDatasetSaver(HF_TOKEN, "crowdsourced-calculator-demo")
+import numpy as np
+import gradio as gr
 
-iface = gr.Interface(
-    calculator,
-    ["number", gr.Radio(["add", "subtract", "multiply", "divide"]), "number"],
-    "number",
-    description="Check out the crowd-sourced dataset at: [https://huggingface.co/Sajjo/crowdsourced-calculator-demo](https://huggingface.co/Sajjo/crowdsourced-calculator-demo)",
-    allow_flagging="manual",
-    flagging_options=["wrong sign", "off by one", "other"],
-    flagging_callback=hf_writer
-)
+def sepia(input_img, strength):
+    sepia_filter = strength * np.array(
+        [[0.393, 0.769, 0.189], [0.349, 0.686, 0.168], [0.272, 0.534, 0.131]]
+    ) + (1-strength) * np.identity(3)
+    sepia_img = input_img.dot(sepia_filter.T)
+    sepia_img /= sepia_img.max()
+    return sepia_img
+
+callback = gr.CSVLogger()
+
+with gr.Blocks() as demo:
+    with gr.Row():
+        with gr.Column():
+            img_input = gr.Image()
+            strength = gr.Slider(0, 1, 0.5)
+        img_output = gr.Image()
+    with gr.Row():
+        btn = gr.Button("Flag")
+        
+    # This needs to be called at some point prior to the first call to callback.flag()
+    callback.setup([img_input, strength, img_output], "flagged_data_points")
+
+    img_input.change(sepia, [img_input, strength], img_output)
+    strength.change(sepia, [img_input, strength], img_output)
+    
+    # We can choose which components to flag -- in this case, we'll flag all of them
+    btn.click(lambda *args: callback.flag(args), [img_input, strength, img_output], None, preprocess=False)
 
-iface.launch()
+demo.launch()