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import gradio as gr
from transformers import AutoModelForSequenceClassification, AutoTokenizer
import torch
# Load the model and tokenizer
model_name = 'FridayMaster/fine_tune_embedding' # Replace with your model's repository name
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForSequenceClassification.from_pretrained(model_name) # Use the appropriate class
# Define a function to generate responses
def generate_response(prompt):
# Tokenize the input prompt
inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=512)
with torch.no_grad():
# Get the model output
outputs = model(**inputs)
# Process the output logits
logits = outputs.logits
predicted_class_id = logits.argmax().item()
# Generate a response based on the predicted class
response = f"Predicted class ID: {predicted_class_id}"
return response
# Create a Gradio interface
iface = gr.Interface(
fn=generate_response,
inputs=gr.Textbox(label="Enter your message", placeholder="Type something here..."),
outputs=gr.Textbox(label="Response"),
title="Chatbot Interface",
description="Interact with the fine-tuned chatbot model."
)
# Launch the Gradio app
if __name__ == "__main__":
iface.launch()
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