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import gradio as gr
import requests
from huggingface_hub import InferenceClient
# Step 1 from Semantic Search
from sentence_transformers import SentenceTransformer
import torch
# Step 2 from Semantic Search
with open("water_cycle.txt", "r", encoding="utf-8") as file:
# Read the entire contents of the file and store it in a variable
water_cycle_text = file.read()
# Print the text below
print(water_cycle_text)
SPOONACULAR_API_KEY = "71259036cfb3405aa5d49c1220a988c5"
def get_recipes(ingredient):
url = "https://api.spoonacular.com/recipes/complexSearch"
params = {
"query": ingredient,
"number": 3,
"apiKey": SPOONACULAR_API_KEY
}
res = requests.get(url, params=params)
data = res.json()
return [r["title"] for r in data["results"]]
iface = gr.Interface(
fn=get_recipes,
inputs="text",
outputs="text",
title="Spoonacular Recipe Finder"
)
iface.launch()
"""
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
"""
demo = gr.ChatInterface(
respond,
additional_inputs=[
gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
gr.Slider(
minimum=0.1,
maximum=1.0,
value=0.95,
step=0.05,
label="Top-p (nucleus sampling)",
),
],
)
if __name__ == "__main__":
demo.launch()
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