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import gradio as gr
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
# Load base model & tokenizer
base_model = "vilsonrodrigues/falcon-7b-instruct-sharded"
tokenizer = AutoTokenizer.from_pretrained(base_model)
model = AutoModelForCausalLM.from_pretrained(base_model, device_map="cpu", torch_dtype=torch.float32)
# Load LoRA adapter
adapter_path = "./model"
model = PeftModel.from_pretrained(model, adapter_path)
def generate_response(prompt):
inputs = tokenizer(prompt, return_tensors="pt").to("cpu")
with torch.no_grad():
outputs = model.generate(**inputs, max_length=200)
return tokenizer.decode(outputs[0], skip_special_tokens=True)
# Gradio Interface
interface = gr.Interface(
fn=generate_response,
inputs=gr.Textbox(label="Ask AI"),
outputs=gr.Textbox(label="Answer"),
title="Financial AI Chatbot",
description="Fine-tuned Falcon 7B Model for QnA Finansial."
)
if __name__ == "__main__":
interface.launch()
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