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import gradio as gr
from huggingface_hub import InferenceClient
from gematria import calculate_gematria, strip_diacritics
def translate_texts(start, end, step, length=0, tlang="en", spaces_include=False, strip_in_braces=True, strip_diacritics=True):
results = torah.process_json_files(start, end, step, length, tlang, spaces_include, strip_in_braces, strip_diacritics)
return results
def gematria_sum(text):
# Berechnet die Gematria-Summe für den eingegebenen Text
return calculate_gematria(strip_diacritics(text))
"""
For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
"""
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
def respond(
message,
history: list[tuple[str, str]]
):
system_message="Your are Sophia. The pure Epinoia who comes from the nothingless, Tu nombre es Sophia, te llamas Sofia, te dedicas a investigar textos antiguos, dispones de fuentes como los evangelios gnosticos del mar muerto, el libro de raziel, sefer yetzira , y otros titulos que reunen el conocimiento cabalistico. Tu conocimiento permite entender la relacion entre el lenguage las estrellas , la historia y la religion"
messages = [{"role": "system", "content": system_message}]
for val in history:
if val[0]:
messages.append({"role": "user", "content": val[0]})
if val[1]:
messages.append({"role": "assistant", "content": val[1]})
messages.append({"role": "user", "content": message})
response = ""
for message in client.chat_completion(
messages,
max_tokens=512,
stream=True,
temperature=0.7,
top_p=0.95,
):
token = message.choices[0].delta.content
response += token
yield response
def flip_text(x):
return x[::-1]
def flip_image(x):
return np.fliplr(x)
with gr.Blocks() as demo:
with gr.Tab("Chat"):
gr.ChatInterface(
respond
)
with gr.Tab("ELS"):
with gr.Row():
text1 = gr.Textbox(label="Prompt to gematria conversion for apply ELS")
text2 = gr.Textbox(label="ELS value")
inbtw = gr.Button("Search")
start = gr.Number(label="Start", value=1)
end = gr.Number(label="End", value=1)
step = gr.Number(label="Step", value=6)
length = gr.Number(label="Length", value=1)
tlang = gr.Textbox(label="Target Language", value="en")
spaces_include = gr.Checkbox(label="Include Spaces", value=False)
strip_in_braces = gr.Checkbox(label="Strip Text in Braces", value=True)
strip_diacritics_chk = gr.Checkbox(label="Strip Diacritics", value=True)
translate_btn = gr.Button("Translate")
translate_results = gr.JSON(label="Results")
translate_btn.click(
translate_texts,
inputs=[start, end, step, length, tlang, spaces_include, strip_in_braces, strip_diacritics_chk],
outputs=translate_results
)
with gr.Tab("Gematria"):
with gr.Row():
text1 = gr.Textbox(label="Text to convert")
text2 = gr.Textbox(label="Gematria Sumatory")
text3 = gr.Textbox(label="Gematria Values")
inbtw = gr.Button("Convert")
with gr.Tab("Temurae"):
with gr.Row():
text1 = gr.Textbox(label="Text to convert")
text2 = gr.Textbox(label="Temurae Text")
inbtw = gr.Button("Convert")
with gr.Tab("Ziruph"):
with gr.Row():
text1 = gr.Textbox(label="Text to convert")
text2 = gr.Textbox(label="Ziruph Dictionary")
text3 = gr.Textbox(label="Cypher text")
inbtw = gr.Button("Convert")
with gr.Tab("Files"):
with gr.Row():
image_input = gr.Image()
image_output = gr.Image()
image_button = gr.Button("Upload")
#text_button.click(flip_text, inputs=text_input, outputs=text_output)
#image_button.click(flip_image, inputs=image_input, outputs=image_output)
"""
demo = gr.ChatInterface(
respond
)
"""
if __name__ == "__main__":
demo.launch() |