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| from fastapi import FastAPI, Request, Form | |
| from fastapi.responses import HTMLResponse | |
| import nest_asyncio | |
| import uvicorn | |
| from transformers import pipeline | |
| app = FastAPI() | |
| async def startup_event(): | |
| model_path="cardiffnlp/twitter-roberta-base-sentiment-latest" | |
| global sentiment_task | |
| sentiment_task = pipeline("sentiment-analysis", model=model_path, tokenizer=model_path) | |
| async def home(): | |
| html_content = """ | |
| <html> | |
| <head> | |
| <title>Text Classification</title> | |
| </head> | |
| <body> | |
| <h1>Text Classification</h1> | |
| <form method="post" action="/analyze/"> | |
| <input type="text" name="text" placeholder="Enter text to analyze" autocomplete="off" required> | |
| <input type="submit" value="Analyze"> | |
| </form> | |
| </body> | |
| </html> | |
| """ | |
| return HTMLResponse(content=html_content, status_code=200) | |
| async def get_name(name: str): | |
| return {'Welcome To Here': f'{name}'} | |
| async def analyze_text(text: str = Form(...)): | |
| # Assuming your model is a function that takes input and returns predictions | |
| prediction = sentiment_task(text) | |
| html_content = """ | |
| <html> | |
| <head> | |
| <title>Analysis Result</title> | |
| </head> | |
| <body> | |
| <h1>Analysis Result:</h1> | |
| <p>Input Text: {input_text}</p> | |
| <p>Prediction: {prediction}</p> | |
| <button><a href="/" >Back</a><button> | |
| </body> | |
| </html> | |
| """.format(input_text=text, prediction=prediction[0]['label']) | |
| return HTMLResponse(content=html_content, status_code=200) | |