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from fastapi import FastAPI, Request, Form | |
from fastapi.responses import HTMLResponse | |
import nest_asyncio | |
import uvicorn | |
# import gradio as gr | |
from tafsir import tafsir | |
app=FastAPI() | |
async def startup_event(): | |
global bot | |
bot=tafsir() | |
async def home(): | |
html_content = """ | |
<html> | |
<head> | |
<title>Tafsir Topic</title> | |
</head> | |
<body> | |
<h1>Topic Input</h1> | |
<form method="post" action="/analyze/"> | |
<input type="text" name="text" placeholder="Enter topic" autocomplete="off" required> | |
<input type="submit" value="Analyze"> | |
</form> | |
</body> | |
</html> | |
""" | |
return HTMLResponse(content=html_content, status_code=200) | |
async def analyze_text(text: str = Form(...)): | |
# Assuming your model is a function that takes input and returns predictions | |
prediction = bot.return_tafsir(text) | |
html_content = """ | |
<html> | |
<head> | |
<title>Analysis Result</title> | |
</head> | |
<body> | |
<h1>Analysis Result:</h1> | |
<p>Topic: {input_text}</p> | |
<p>Top 3 results: {prediction}</p> | |
<button><a href="/" >Back</a><button> | |
</body> | |
</html> | |
""".format(input_text=text, prediction=prediction) | |
return HTMLResponse(content=html_content, status_code=200) | |
async def test(): | |
# Assuming your model is a function that takes input and returns predictions | |
prediction = bot.return_tafsir("tolerance") | |
print(prediction) | |
return "finished" | |
def greet(inputs): | |
return "hello "+inputs | |
# @app.get("/gradio") | |
# async def gradio_test(): | |
# iface = gr.Interface(fn=greet, inputs= [ | |
# gr.Textbox( | |
# label="Input", | |
# info="Find ambiguities in the following", | |
# lines=3, | |
# value="The test can only continue if it receives all inputs from previous page.", | |
# ), | |
# ], outputs= gr.Textbox( | |
# label="Input", | |
# info="Find ambiguities in the following", | |
# lines=3, | |
# value="The test can only continue if it receives all inputs from previous page.", | |
# ), | |
# theme=gr.themes.Base()) | |
# iface.launch() |