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from langchain_community.document_loaders import PyPDFLoader
from langchain_core.messages import AIMessage, HumanMessage
from pydantic import BaseModel
import rag
import time
import gradio as gr
import requests
from main import run_server
class ChatInput(BaseModel):
question: str
chat_history = []
def generate_response(chat_input: str, bot_message: str) -> str:
url = "http://127.0.0.1:8000/generatechat/"
payload = {
'question': chat_input,
}
headers = {
'Content-Type': 'application/json'
}
response = requests.post(url, json=payload, headers=headers)
if response.status_code == 200:
data = response.json()
answer = data['response']['answer']
print("Success:", response.json())
# Get a typewriting animation response
partial_response = ""
for char in answer:
partial_response += char
yield partial_response
time.sleep(0.005)
else:
print("Error:", response.status_code, response.text)
return f"Error: {response.status_code}, {response.text}"
with gr.Blocks() as demo:
with gr.Column():
chatbot = gr.ChatInterface(
fn=generate_response,
title="ThaiCodex Chat",
description="Ask questions based on the content of the uploaded or specified PDF.",
)
# with gr.Row():
# pdf_input = gr.File(label="Upload PDF", file_types=[".pdf"])
# upload_button = gr.Button("Load PDF")
output_text = gr.Textbox(label="Status")
# upload_button.click(, inputs=[pdf_input], outputs=output_text)
if __name__ == "__main__":
demo.launch()
run_server() # uvicorn api |