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app.py
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from flask import Flask, render_template, request, jsonify
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import logging
app = Flask(__name__)
# Set up logging
logging.basicConfig(level=logging.DEBUG)
# Path to the fine-tuned model
model_path = "./finetuned_model"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path)
# Add a padding token
tokenizer.pad_token = tokenizer.eos_token
@app.route("/")
def index():
return render_template('index.html')
@app.route("/get", methods=["POST"])
def chat():
try:
msg = request.form["msg"]
logging.debug(f"Received message: {msg}")
response = get_chat_response(msg)
logging.debug(f"Generated response: {response}")
return response
except Exception as e:
logging.error(f"Error in chat endpoint: {str(e)}")
return "Error: " + str(e), 500
def get_chat_response(text):
try:
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
outputs = model.generate(inputs.input_ids, max_length=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
except Exception as e:
logging.error(f"Error in get_chat_response: {str(e)}")
return "Error: " + str(e)
if __name__ == '__main__':
app.run(debug=False) # Set debug=False
=======
from flask import Flask, render_template, request, jsonify
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
import logging
app = Flask(__name__)
# Set up logging
logging.basicConfig(level=logging.DEBUG)
# Path to the fine-tuned model
model_path = "./finetuned_model"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path)
# Add a padding token
tokenizer.pad_token = tokenizer.eos_token
@app.route("/")
def index():
return render_template('index.html')
@app.route("/get", methods=["POST"])
def chat():
try:
msg = request.form["msg"]
logging.debug(f"Received message: {msg}")
response = get_chat_response(msg)
logging.debug(f"Generated response: {response}")
return response
except Exception as e:
logging.error(f"Error in chat endpoint: {str(e)}")
return "Error: " + str(e), 500
def get_chat_response(text):
try:
inputs = tokenizer(text, return_tensors="pt", padding=True, truncation=True, max_length=512)
outputs = model.generate(inputs.input_ids, max_length=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
except Exception as e:
logging.error(f"Error in get_chat_response: {str(e)}")
return "Error: " + str(e)
if __name__ == '__main__':
app.run(debug=False) # Set debug=False
>>>>>>> 6000d4c4d433697fd933fc82e125af8c55b5fed5