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| import os | |
| import subprocess | |
| # Function to install a package if it is not already installed | |
| def install(package): | |
| subprocess.check_call([os.sys.executable, "-m", "pip", "install", package]) | |
| # Ensure the necessary packages are installed | |
| install("transformers") | |
| install("torch") | |
| install("pandas") | |
| install("gradio") | |
| import pandas as pd | |
| import gradio as gr | |
| from transformers import AutoModel, AutoTokenizer | |
| import torch | |
| # Load the dataset containing PEC numbers and names | |
| def load_dataset(file_path='PEC_Numbers_and_Names.xlsx'): | |
| if not os.path.exists(file_path): | |
| raise FileNotFoundError(f"File not found: {file_path}") | |
| df = pd.read_excel(file_path) | |
| return df | |
| # Debugging function to get PEC number based on the name | |
| def get_pec_number(name, df): | |
| print("Column names in DataFrame:", df.columns.tolist()) # Print the column names | |
| print(f"Looking for Name: '{name}'") | |
| # Normalize the input and dataset | |
| df['Name'] = df['Name'].str.strip().str.lower() | |
| name = name.strip().str.lower() | |
| result = df[df['Name'] == name] | |
| if not result.empty: | |
| print(f"Found PEC Number: {result.iloc[0]['PEC No.']}") | |
| return result.iloc[0]['PEC No.'] | |
| else: | |
| print("Name not found.") | |
| return "Name not found." | |
| # Function to process the name using the Hugging Face model | |
| def process_with_model(name): | |
| inputs = tokenizer(name, return_tensors="pt") | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| return outputs.last_hidden_state.mean(dim=1).squeeze().tolist() | |
| # Combine both functions to create a prediction | |
| def predict(name, file): | |
| try: | |
| # Load the dataset from the uploaded file if provided | |
| if file is not None: | |
| df = pd.read_excel(file.name) | |
| else: | |
| df = load_dataset() | |
| pec_number = get_pec_number(name, df) | |
| model_output = process_with_model(name) | |
| return f"PEC Number: {pec_number}\nModel Output: {model_output}" | |
| except FileNotFoundError as e: | |
| return str(e) | |
| # Load the model and tokenizer from Hugging Face | |
| tokenizer = AutoTokenizer.from_pretrained("Alibaba-NLP/gte-multilingual-base", trust_remote_code=True) | |
| model = AutoModel.from_pretrained("Alibaba-NLP/gte-multilingual-base", trust_remote_code=True) | |
| # Build the Gradio interface with file upload option | |
| iface = gr.Interface( | |
| fn=predict, | |
| inputs=[ | |
| gr.Textbox(lines=1, placeholder="Enter Name..."), | |
| gr.File(label="Upload PEC Numbers and Names file (optional)") | |
| ], | |
| outputs="text", | |
| title="Name to PEC Number Lookup with Model Integration", | |
| description="Enter a name to retrieve the corresponding PEC number and process it with a Hugging Face model. Optionally, upload the Excel file if not found." | |
| ) | |
| # Run the Gradio interface | |
| if __name__ == "__main__": | |
| iface.launch() | |