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import gradio as gr
from huggingface_hub import InferenceClient
import gen
import psychohistory
"""
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,
max_tokens,
temperature,
top_p,
):
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=max_tokens,
stream=True,
temperature=temperature,
top_p=top_p,
):
token = message.choices[0].delta.content
response += token
yield response
"""
For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
"""
with gr.Blocks(title="PSYCHOHISTORY") as app:
with gr.Tab("Search"):
with gr.Row():
txt_search = gr.Textbox(value="Iran and Israel war",label="Search Term",scale=5)
btn_search = gr.Button("Look",scale=1)
with gr.Row():
#search_results = gr.Dataframe(type="pandas")
mem_results = gr.JSON(label="Results")
btn_search.click(
gen.generate,
inputs=[txt_search],
outputs=mem_results
)
#with gr.Row():
# big_block = gr.HTML("""
# <iframe style="scroll-padding-left: 50%; relative;background-color: #fff; height: 75vh; width: 100%; overflow-y: hidden; overflow-x: hidden;" src="https://holmesbot.com/api/shared?id=16657e456d9514"></iframe>
# """)
with gr.Tab("Graph"):
gr.load("models/stabilityai/stable-diffusion-xl-base-1.0")
with gr.Tab("Chat"):
gr.ChatInterface(
respond,
)
if __name__ == "__main__":
app.launch() |