app.py

import gradio as gr
from transformers import AutoTokenizer, TFBlenderbotForConditionalGeneration
import tensorflow as tf
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)

print("Interface getting done....")
# Define the chatbot function
def predict(user_input):
    # Tokenize input text
    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)
    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()