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5524045 72e2a6e 5524045 03a5abf 5524045 03a5abf 5524045 03a5abf 5524045 03a5abf 5524045 03a5abf | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 | from flask import Flask, render_template, request
from transformers import GPT2LMHeadModel, GPT2Tokenizer
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.metrics.pairwise import cosine_similarity
import json
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
model = GPT2LMHeadModel.from_pretrained('gpt2')
json_file_path = 'short_let_room.json'
app = Flask(__name__)
def generate_gpt2_response(question):
input_ids = tokenizer.encode(question, return_tensors='pt')
generated_output = model.generate(input_ids, max_length=len(input_ids[0]) + 100,
num_beams=5,
no_repeat_ngram_size=2,
top_k=10,
top_p=1,
temperature=0.9,
pad_token_id=model.config.eos_token_id)
generated_response = tokenizer.decode(generated_output[0], skip_special_tokens=True)
return generated_response
def find_question_and_answer(json_file, question, input_ids):
with open(json_file, "r") as json_file:
data = json.load(json_file)
quest = question.lower()
all_questions = data.get("questions", [])
all_response = data.get("responses", [])
for dataset_question in all_questions:
if 'question' in dataset_question and dataset_question['question'].lower() == quest:
for dataset_response in all_response:
if dataset_question['response_id'] == dataset_response['id']:
response = {
"Chat Bot ": dataset_response["response_message"],
"Apartment ": dataset_response["response_message1"],
"Address ": dataset_response["shortlet_Address"],
"Price ": dataset_response["shortlet_Price"],
"URL ": dataset_response.get("shortlet_url")
}
return response
input_ids = tokenizer.encode(question, return_tensors='pt')
generated_output = model.generate(input_ids, max_length=len(input_ids[0]) + 100,
num_beams=5,
no_repeat_ngram_size=2,
top_k=10,
top_p=1,
temperature=0.9,
pad_token_id=model.config.eos_token_id)
generated_response = tokenizer.decode(generated_output[0], skip_special_tokens=True)
return generated_response
# @app.route("/")
# def index():
# return render_template('index.html')
# @app.route('/ask', methods=['POST'])
# def ask():
# user_input = request.form['user_input']
# if not user_input:
# response = ""
# else:
# input_ids = tokenizer.encode(user_input, return_tensors='pt')
# response = find_question_and_answer(json_file_path, user_input, input_ids)
# if isinstance(response, dict):
# response_type = "mapping"
# else:
# response_type = "string"
# return render_template('index.html', user_input=user_input, response=response, response_type=response_type)
if __name__ == '__main__':
user_input = st.text_area("Ask Haven AI: ")
response = find_question_and_answer(json_file_path, user_input)
st.write(response)
app.run(debug=True)
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