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import gradio as gr
import requests
from g4f import Provider, models
from langchain.llms.base import LLM
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
)
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:
system_prompt: |
Agis en tant qu'assistant juridique gabonais Répons au question en français et en citant les articles .
""")
print(res)
return res
iface = gr.Interface(fn=greet, inputs="text", outputs="text")
iface.launch() |