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import gradio as gr
from transformers import AutoTokenizer, TFBlenderbotForConditionalGeneration
import tensorflow as tf
import json
import os
data = {"Interactions":[]}
with open("question_answer.json", "w") as file:
json.dump(data, file, indent=4)
print("Loading the model......")
model_name = "WICKED4950/Irisonego5"
strategy = tf.distribute.MirroredStrategy()
tf.config.optimizer.set_jit(True) # Enable XLA
tokenizer = AutoTokenizer.from_pretrained(model_name)
with strategy.scope():
model = TFBlenderbotForConditionalGeneration.from_pretrained(model_name)
def save_question(question,answer,path = "question_answer.json"):
with open(path, "r") as file:
data = json.load(file)
data["Interactions"].append({"Question:":question,"Answer:":answer})
print(data)
with open(path, "w") as file:
json.dump(data, file, indent=4)
print("Interface getting done....")
# Define the chatbot function
def predict(user_input):
if user_input == "Print_data_hmm":
print(json.dumps(data, indent=4))
return "Done"
else:
inputs = tokenizer(user_input, return_tensors="tf", padding=True, truncation=True)
# Generate the response using the model
response_id = model.generate(
inputs['input_ids'],
max_length=128, # Set max length of response
do_sample=True, # Sampling for variability
top_k=15, # Consider top 50 tokens
top_p=0.95, # Nucleus sampling
temperature=0.8 # Adjusts creativity of response
)
# Decode the response
response = tokenizer.decode(response_id[0], skip_special_tokens=True)
save_question(question = user_input,answer=response)
return response
# Gradio interface
gr.Interface(
fn=predict,
inputs=gr.Textbox(label="Ask Iris anything!"),
outputs=gr.Textbox(label="Iris's Response"),
examples=[
["What should I do if I'm feeling down?"],
["How do I deal with stress?"],
["Tell me something positive!"]
],
description="A chatbot trained to provide friendly and comforting responses. Type your question below and let Iris help!",
title="Iris - Your Friendly Mental Health Assistant",
).launch()
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