app.py

# Install necessary libraries
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("scikit-learn")
install("gradio")
import os
import pandas as pd
import gradio as gr
from transformers import AutoModel, AutoTokenizer
import torch
from sklearn.model_selection import train_test_split

# Load your dataset
def load_dataset():
    file_path = "Valid-part-2.xlsx"
    print(f"Current working directory: {os.getcwd()}")
    if not os.path.exists(file_path):
        raise FileNotFoundError(f"Dataset not found. Please ensure that '{file_path}' exists.")
    
    try:
        df = pd.read_excel(file_path)
        print("Columns in the dataset:", df.columns.tolist())
        return df
    except Exception as e:
        print(f"Error loading dataset: {e}")
        return None

# Preprocess the data
def preprocess_data(df):
    # Example preprocessing: You can add more steps as needed
    # For now, we're just returning the dataframe as is
    return df

# Train your model
def train_model(df):
    # Split the dataset into training and testing sets
    train_df, test_df = train_test_split(df, test_size=0.2, random_state=42)
    
    # Load your pre-trained model and tokenizer from Hugging Face
    tokenizer = AutoTokenizer.from_pretrained("Alibaba-NLP/gte-multilingual-base")
    model = AutoModel.from_pretrained("Alibaba-NLP/gte-multilingual-base")

    # Normally, you would fine-tune the model here with your training data
    # Since this is an example, we're returning the model as is
    return model

# Define the Gradio interface function
def predict(input_text):
    # Load the model and tokenizer
    tokenizer = AutoTokenizer.from_pretrained("Alibaba-NLP/gte-multilingual-base")
    model = AutoModel.from_pretrained("Alibaba-NLP/gte-multilingual-base")
    
    # Tokenize input and make predictions
    inputs = tokenizer(input_text, return_tensors="pt")
    with torch.no_grad():
        outputs = model(**inputs)
    
    # Process the outputs as needed (e.g., extracting relevant information)
    return outputs.last_hidden_state

# Build the Gradio interface
def build_interface():
    df = load_dataset()  # Load your dataset
    if df is None:
        return None

    df = preprocess_data(df)  # Preprocess the dataset
    model = train_model(df)  # Train your model
    
    iface = gr.Interface(
        fn=predict,
        inputs=gr.inputs.Textbox(lines=2, placeholder="Enter text here..."),
        outputs="text"
    )
    return iface

# Run the Gradio interface
if __name__ == "__main__":
    iface = build_interface()
    if iface:
        iface.launch()
    else:
        print("Failed to build the Gradio interface. Please check the dataset and model.")