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from flask import Flask, request, render_template
from transformers import pipeline
app = Flask(__name__)
nlp = pipeline('sentiment-analysis')
@app.route('/')
def home():
return render_template('index.html')
@app.route('/predict',methods=['POST'])
def predict():
if request.method == 'POST':
message = request.form['message']
prediction = nlp(message)
return render_template('index.html', prediction_text=prediction)
if __name__ == "__main__":
app.run(debug=True)
from transformers import GPT3LMHeadModel, GPT2Tokenizer
tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
model = GPT3LMHeadModel.from_pretrained("gpt3")
def get_response(prompt):
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(inputs, max_length=150, num_return_sequences=1, no_repeat_ngram_size=2)
response = tokenizer.decode(outputs[:, inputs.shape[-1]:][0], skip_special_tokens=True)
return response
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