Spaces:
Sleeping
Sleeping
chore: version 6
Browse files
app.py
CHANGED
@@ -144,12 +144,10 @@ def get_features_fn(*checked_symptoms: Tuple[str]) -> Dict:
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return {
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error_box1: gr.update(visible=False),
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user_vect_box1: gr.update(
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-
visible=
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-
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recap_symptoms_box: gr.update(
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visible=True,
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value=pretty_print(checked_symptoms, case_conversion=str.capitalize, delimiter=", "),
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),
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}
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@@ -242,8 +240,7 @@ def encrypt_fn(user_symptoms: np.ndarray, user_id: str) -> None:
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return {
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error_box3: gr.update(visible=False),
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-
user_vect_box2: gr.update(visible=
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quant_vect_box: gr.update(visible=False, value=quant_user_symptoms),
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enc_vect_box: gr.update(visible=True, value=encrypted_quantized_user_symptoms_shorten_hex),
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}
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@@ -406,7 +403,9 @@ def get_output_fn(user_id: str, user_symptoms: np.ndarray) -> Dict:
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return {error_box6: gr.update(visible=False), srv_resp_retrieve_data_box: "Data received"}
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-
def decrypt_fn(
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"""Dencrypt the data on the `Client Side`.
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Args:
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@@ -464,15 +463,15 @@ def decrypt_fn(user_id: str, user_symptoms: np.ndarray, threshold: int = 0.5) ->
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or (np.sum(top3_proba) < threshold)
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or (abs(top3_proba[0] - top3_proba[1]) < threshold)
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):
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out =
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-
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"Here are the top3 predictions:"
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-
)
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else:
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out = "
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out = (
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f"{out}
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f"1. « {get_disease_name(top3_diseases[0])} » with a probability of {top3_proba[0]:.2%}\n"
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f"2. « {get_disease_name(top3_diseases[1])} » with a probability of {top3_proba[1]:.2%}\n"
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f"3. « {get_disease_name(top3_diseases[2])} » with a probability of {top3_proba[2]:.2%}\n"
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@@ -490,12 +489,13 @@ def reset_fn():
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clean_directory()
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return {
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user_id_box: gr.update(visible=False, value=None, interactive=False),
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user_vect_box1: None,
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recap_symptoms_box: gr.update(visible=False, value=None),
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default_symptoms: gr.update(visible=True, value=None),
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disease_box: gr.update(visible=True, value=None),
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user_vect_box2: gr.update(visible=False, value=None, interactive=False),
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quant_vect_box: gr.update(visible=False, value=None, interactive=False),
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enc_vect_box: gr.update(visible=True, value=None, interactive=False),
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key_box: gr.update(visible=True, value=None, interactive=False),
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@@ -536,7 +536,7 @@ CSS = """
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</button>
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"""
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back_to_top_btn_html =
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<button onclick="scrollToTop()" style="color:white; text-decoration:none;">
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Back to Top!
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@@ -555,7 +555,7 @@ function scrollToTop() {
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</script>
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-
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if __name__ == "__main__":
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@@ -565,7 +565,7 @@ if __name__ == "__main__":
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(X_train, X_test), (y_train, y_test), valid_symptoms, diseases = load_data()
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with gr.Blocks(css="
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# Link + images
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gr.Markdown(
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@@ -610,8 +610,9 @@ if __name__ == "__main__":
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with gr.Tabs(eelem_id="them") as tabs:
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with gr.TabItem("1. Chief Complaints", id=0):
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gr.Markdown("<span style='color:grey'>Client Side</span>")
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gr.Markdown(
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-
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# Box symptoms
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check_boxes = []
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@@ -633,7 +634,7 @@ if __name__ == "__main__":
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# Default disease, picked from the dataframe
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gr.Markdown(
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"You can choose an existing disease and explore its associated symptoms."
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)
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with gr.Row():
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@@ -648,18 +649,24 @@ if __name__ == "__main__":
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fn=display_default_symptoms_fn, inputs=[disease_box], outputs=[default_symptoms]
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)
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-
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-
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-
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-
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# Clear botton
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clear_button = gr.Button("Reset Space 🔁")
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-
# Next tab
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gr.Markdown("")
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next_tab = gr.Button("Next Step 👉")
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next_tab.click(lambda _: gr.Tabs.update(selected=1), None, tabs)
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with gr.TabItem("2. Data Encryption", id=1):
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gr.Markdown("<span style='color:grey'>Client Side</span>")
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@@ -672,15 +679,10 @@ if __name__ == "__main__":
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gen_key_btn = gr.Button("Generate the evaluation key 👆")
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error_box2 = gr.Textbox(label="Error ❌", visible=False)
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-
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user_id_box = gr.Textbox(label="User ID:", interactive=False, visible=True)
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# Evaluation key size
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-
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key_len_box = gr.Textbox(
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label="Evaluation Key Size:", interactive=False, visible=False
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)
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-
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# Evaluation key (truncated)
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key_box = gr.Textbox(
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label="Evaluation key (truncated):",
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max_lines=3,
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@@ -688,41 +690,26 @@ if __name__ == "__main__":
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visible=False,
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)
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gen_key_btn.click(
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key_gen_fn,
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inputs=user_vect_box1,
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outputs=[
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key_box,
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user_id_box,
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key_len_box,
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error_box2,
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],
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)
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gr.Markdown("## Encrypt the data")
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encrypt_btn = gr.Button("Encrypt the data using the 🔒 private secret key 👆")
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error_box3 = gr.Textbox(label="Error ❌", visible=False)
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with gr.Row():
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with gr.Column(
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user_vect_box2 = gr.Textbox(
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label="User Symptoms Vector:",
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interactive=False,
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visible=False,
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)
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with gr.Column(
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quant_vect_box = gr.Textbox(
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label="Quantized Vector:",
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interactive=False,
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visible=False,
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)
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-
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with gr.Column(scale=1, min_width=600):
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enc_vect_box = gr.Textbox(
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label="Encrypted Vector:",
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max_lines=
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interactive=False,
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)
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@@ -731,7 +718,6 @@ if __name__ == "__main__":
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inputs=[user_vect_box1, user_id_box],
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outputs=[
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user_vect_box2,
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quant_vect_box,
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enc_vect_box,
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error_box3,
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],
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@@ -832,7 +818,7 @@ if __name__ == "__main__":
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decrypt_target_btn.click(
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decrypt_fn,
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inputs=[user_id_box, user_vect_box1],
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outputs=[decrypt_target_box, error_box7],
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)
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@@ -845,17 +831,29 @@ if __name__ == "__main__":
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next_tab = gr.Button("👈 👈 Go back to start")
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next_tab.click(lambda _: gr.Tabs.update(selected=0), None, tabs)
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submit_button.click(
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fn=get_features_fn,
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inputs=[*check_boxes],
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-
outputs=[user_vect_box1, error_box1,
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)
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clear_button.click(
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reset_fn,
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outputs=[
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user_vect_box1,
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user_vect_box2,
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# disease_box,
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error_box1,
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error_box2,
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return {
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error_box1: gr.update(visible=False),
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user_vect_box1: gr.update(
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visible=False,
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value=get_user_symptoms_from_checkboxgroup(pretty_print(checked_symptoms)),
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),
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submit_button: gr.update(value="Data Submitted ✅"),
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}
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return {
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error_box3: gr.update(visible=False),
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user_vect_box2: gr.update(visible=True, value=user_symptoms),
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enc_vect_box: gr.update(visible=True, value=encrypted_quantized_user_symptoms_shorten_hex),
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}
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return {error_box6: gr.update(visible=False), srv_resp_retrieve_data_box: "Data received"}
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+
def decrypt_fn(
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user_id: str, user_symptoms: np.ndarray, *checked_symptoms, threshold: int = 0.5
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+
) -> Dict:
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"""Dencrypt the data on the `Client Side`.
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Args:
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or (np.sum(top3_proba) < threshold)
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or (abs(top3_proba[0] - top3_proba[1]) < threshold)
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):
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+
out = "⚠️ The prediction appears uncertain; including more symptoms may improve the results.\n\n"
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+
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else:
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out = ""
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out = (
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f"{out}"
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f"Given the symptoms you provided: {pretty_print(checked_symptoms, case_conversion=str.capitalize, delimiter=', ')}\n\n"
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"Here are the top3 predictions:\n\n"
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f"1. « {get_disease_name(top3_diseases[0])} » with a probability of {top3_proba[0]:.2%}\n"
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f"2. « {get_disease_name(top3_diseases[1])} » with a probability of {top3_proba[1]:.2%}\n"
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f"3. « {get_disease_name(top3_diseases[2])} » with a probability of {top3_proba[2]:.2%}\n"
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clean_directory()
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return {
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+
user_vect_box2: None,
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submit_button: gr.update(value="Confirm Symptoms"),
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user_id_box: gr.update(visible=False, value=None, interactive=False),
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user_vect_box1: None,
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recap_symptoms_box: gr.update(visible=False, value=None),
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default_symptoms: gr.update(visible=True, value=None),
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disease_box: gr.update(visible=True, value=None),
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quant_vect_box: gr.update(visible=False, value=None, interactive=False),
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enc_vect_box: gr.update(visible=True, value=None, interactive=False),
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key_box: gr.update(visible=True, value=None, interactive=False),
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</button>
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"""
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+
back_to_top_btn_html = """
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<button onclick="scrollToTop()" style="color:white; text-decoration:none;">
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Back to Top!
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</script>
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+
"""
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if __name__ == "__main__":
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(X_train, X_test), (y_train, y_test), valid_symptoms, diseases = load_data()
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+
with gr.Blocks(css="them") as demo:
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# Link + images
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gr.Markdown(
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with gr.Tabs(eelem_id="them") as tabs:
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with gr.TabItem("1. Chief Complaints", id=0):
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gr.Markdown("<span style='color:grey'>Client Side</span>")
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+
gr.Markdown(
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"## Provide at least 5 chief complaints by filling in the boxes below. "
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)
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# Box symptoms
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check_boxes = []
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# Default disease, picked from the dataframe
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gr.Markdown(
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"## You can choose an **existing disease** and explore its associated symptoms."
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)
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with gr.Row():
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fn=display_default_symptoms_fn, inputs=[disease_box], outputs=[default_symptoms]
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)
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+
gr.Markdown(
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"#### Submit your chief complaints by clicking on **Confirm Symptoms 👆** then go to the **Next Step 👉**"
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)
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user_vect_box1 = gr.Textbox(
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visible=False,
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)
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with gr.Row():
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with gr.Column():
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# Submit botton
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submit_button = gr.Button("Confirm Symptoms 👆")
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with gr.Column():
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next_tab = gr.Button("Next Step 👉")
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next_tab.click(lambda _: gr.Tabs.update(selected=1), None, tabs)
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# Clear botton
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clear_button = gr.Button("Reset Space 🔁")
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with gr.TabItem("2. Data Encryption", id=1):
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gr.Markdown("<span style='color:grey'>Client Side</span>")
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gen_key_btn = gr.Button("Generate the evaluation key 👆")
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error_box2 = gr.Textbox(label="Error ❌", visible=False)
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user_id_box = gr.Textbox(label="User ID:", interactive=False, visible=True)
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key_len_box = gr.Textbox(
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label="Evaluation Key Size:", interactive=False, visible=False
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)
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key_box = gr.Textbox(
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label="Evaluation key (truncated):",
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max_lines=3,
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visible=False,
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)
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gr.Markdown("## Encrypt the data")
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encrypt_btn = gr.Button("Encrypt the data using the 🔒 private secret key 👆")
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error_box3 = gr.Textbox(label="Error ❌", visible=False)
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quant_vect_box = gr.Textbox(
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label="Quantized Vector:",
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interactive=False,
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+
visible=False,
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)
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with gr.Row():
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with gr.Column():
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user_vect_box2 = gr.Textbox(
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label="User Symptoms Vector:", interactive=False, max_lines=10
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)
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+
with gr.Column():
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enc_vect_box = gr.Textbox(
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label="Encrypted Vector:",
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+
max_lines=10,
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interactive=False,
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)
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715 |
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inputs=[user_vect_box1, user_id_box],
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outputs=[
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user_vect_box2,
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enc_vect_box,
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error_box3,
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],
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decrypt_target_btn.click(
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decrypt_fn,
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inputs=[user_id_box, user_vect_box1, *check_boxes],
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outputs=[decrypt_target_box, error_box7],
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)
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next_tab = gr.Button("👈 👈 Go back to start")
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next_tab.click(lambda _: gr.Tabs.update(selected=0), None, tabs)
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gen_key_btn.click(
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key_gen_fn,
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inputs=user_vect_box1,
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outputs=[
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key_box,
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user_id_box,
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key_len_box,
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error_box2,
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],
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)
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844 |
+
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submit_button.click(
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fn=get_features_fn,
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inputs=[*check_boxes],
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+
outputs=[user_vect_box1, error_box1, submit_button],
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849 |
)
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850 |
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clear_button.click(
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reset_fn,
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outputs=[
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user_vect_box2,
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+
user_vect_box1,
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submit_button,
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# disease_box,
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error_box1,
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error_box2,
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