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

# Function to convert a list to a DataFrame
def list_to_dataframe(data_list):
    # Convert the list to a DataFrame (assuming it's a list of dicts or tuples)
    df = pd.DataFrame(data_list)
    return df

# Load your dataset from a file
def load_dataset(file_path=None):
    if file_path is None:
        file_path = '/content/Valid-part-2.xlsx'  # Default path if the file is uploaded manually to Colab

    # Check if the file exists
    if file_path and not os.path.exists(file_path):
        print(f"File not found at '{file_path}', using default list data...")
        # Fallback to a default list if file is not found
        default_data = [
            {'text': 'Example sentence 1', 'label': 'label1'},
            {'text': 'Example sentence 2', 'label': 'label2'},
            # Add more example data as needed
        ]
        return list_to_dataframe(default_data)
    
    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):
    # Add your preprocessing steps here
    # For example: cleaning, tokenization, etc.
    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", trust_remote_code=True)
    model = AutoModel.from_pretrained("Alibaba-NLP/gte-multilingual-base", trust_remote_code=True)

    # Add your training code here
    # This may involve tokenizing the data and feeding it into the model
    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", trust_remote_code=True)
    model = AutoModel.from_pretrained("Alibaba-NLP/gte-multilingual-base", trust_remote_code=True)
    
    # 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(file_path=None):
    df = load_dataset(file_path)  # 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__":
    # You can specify a file_path here if you have a specific file to use
    file_path = None  # Change this to your specific file path if needed
    iface = build_interface(file_path=file_path)
    if iface:
        iface.launch()
    else:
        print("Failed to build the Gradio interface. Please check the dataset and model.")