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import gradio as gr
import pandas as pd
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the model and tokenizer
model_name = "Qwen/Qwen3-0.6B-Base"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
def process_excel(file, prompt):
# Read the Excel file
df = pd.read_excel(file.name)
# Convert the DataFrame to a string representation
excel_content = df.to_string(index=False)
# Combine the prompt with the Excel content
full_prompt = f"{prompt}\n\nExcel Data:\n{excel_content}"
# Tokenize the input and generate a response
inputs = tokenizer(full_prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=500)
# Decode the output
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
return result
# Define the Gradio interface
app = gr.Interface(
fn=process_excel,
inputs=[
gr.File(label="Upload Excel File"),
gr.Textbox(label="Enter your prompt")
],
outputs=gr.Textbox(label="Result"),
title="Excel Processing with Qwen3",
description="Upload an Excel file and enter a prompt to process the data."
)
# Launch the app
app.launch(server_name="0.0.0.0", server_port=7860)
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