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import gradio as gr
import requests
from g4f import Provider, models
from langchain.llms.base import LLM
import g4f
from langchain_g4f import G4FLLM
g4f.debug.logging = True # Enable logging
g4f.check_version = False # Disable automatic version checking
#print(g4f.version) # Check version
print(g4f.Provider.Ails.params) # Supported args
url = "https://app.embedchain.ai/api/v1/pipelines/f14b3df8-db63-456c-8a7f-4323b4467271/context/"
def greet(name):
payload = {
"query": f"{name}",
"count": 15
}
headers = {
'Authorization': 'Token ec-pbVFWamfNAciPwb18ZwaQkKKUCCBnafko9ydl3Y5',
}
response = requests.request("POST", url, headers=headers, json=payload)
print(name)
c = response.text
llm = LLM = G4FLLM(model=models.gpt_35_turbo_16k)
def hein(q):
re = llm(q)
return re
with gr.Blocks() as demo:
chatbot = gr.Chatbot()
msg = gr.Textbox()
clear = gr.ClearButton([msg, chatbot])
def respond(message, chat_history):
print(message)
print("ok")
print(chat_history)
res = hein(q)
bot_message = res
chat_history.append((message,bot_message))
time.sleep(2)
return "", chat_history
msg.submit(respond, [msg, chatbot], [msg, chatbot])
demo.launch()
#iface = gr.Interface(fn=greet, inputs="text", outputs="text")
#iface.launch() |