app.py

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):
    inputs = tokenizer(prompt, return_tensors="pt", padding=True, truncation=True, max_length=512)
    with torch.no_grad():
        outputs = model(**inputs)
    # Customize the response generation as per your model's output
    response = tokenizer.decode(outputs.logits.argmax(dim=-1), skip_special_tokens=True)
    return response

# Create a Gradio interface
iface = gr.Interface(
    fn=generate_response,
    inputs=gr.inputs.Textbox(label="Enter your message", placeholder="Type something here..."),
    outputs=gr.outputs.Textbox(label="Response"),
    title="Chatbot Interface",
    description="Interact with the fine-tuned chatbot model."
)

# Launch the Gradio app
if __name__ == "__main__":
    iface.launch()