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Update app.py
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app.py
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from fastapi import FastAPI
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from transformers import pipeline
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app=FastAPI()
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# Initialize the text generation pipeline
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pipe = pipeline("text2text-generation", model="google/flan-t5-small")
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@app.get("/")
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def home():
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# Define a function to handle the GET request at `/generate`
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from fastapi import FastAPI, Request
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from fastapi.responses import HTMLResponse
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from transformers import pipeline
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# Initialize the FastAPI app
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app = FastAPI()
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# Initialize the text generation pipeline
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pipe = pipeline("text2text-generation", model="google/flan-t5-small")
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@app.get("/", response_class=HTMLResponse)
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def home():
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"""
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Renders a simple HTML page with a text input field and a submit button.
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"""
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return """
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Text Generation</title>
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</head>
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<body>
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<h1>Text Generation with FastAPI and Hugging Face Transformers</h1>
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<form action="/generate" method="get">
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<label for="text">Enter Text:</label><br><br>
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<input type="text" id="text" name="text" required><br><br>
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<button type="submit">Generate</button>
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</form>
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<div id="result"></div>
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</body>
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</html>
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"""
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@app.get("/generate", response_class=HTMLResponse)
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def generate(text: str):
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"""
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Handles the text generation and returns the generated output in HTML format.
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"""
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# Use the pipeline to generate text from the given input text
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output = pipe(text)
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# Prepare the result as an HTML response
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generated_text = output[0]['generated_text']
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return f"""
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<!DOCTYPE html>
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<html lang="en">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Generated Output</title>
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</head>
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<body>
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<h1>Generated Text</h1>
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<p><strong>Input:</strong> {text}</p>
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<p><strong>Output:</strong> {generated_text}</p>
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<br>
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<a href="/">Go Back</a>
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</body>
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</html>
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"""
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# Run the app with: uvicorn your_filename:app --reload
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