app.py

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/024a60fa-cfc3-41a2-a27b-2f6a04c1a6fe/context/"


def greet(name):
  payload = {
  "query": f"{name}",
  "count": 25 }
  headers = {
  'Authorization': 'Token ec-fBwP02l3yodIa40BHkSEdhqVQmelK8pNsbrUew2J',}
  response = requests.request("POST", url, headers=headers, json=payload)
  
  print(response.text)
  print(name)
  c = response.text
  llm = LLM = G4FLLM(model=models.gpt_35_turbo_16k    )
  res = llm(f"""
      Use the following pieces of context to answer the query at the end.
      If you don't know the answer, just say that you don't know, don't try to make up an answer.

      ${c}

      Query: ${name}

      Helpful Answer:
    
    """)
  print(res) 
  return res



iface = gr.Interface(
    fn=greet,
    inputs="text",
    outputs=gr.Textbox(label="Réponse"),
    title="bot",
    description=" Chatbot-law-code-pénal ")

iface.launch()