Main.py

import gradio as gr
import numpy as np
import tensorflow as tf
from transformers import AutoTokenizer, TFAutoModelForSequenceClassification

# Function to load the model
def load_model():
    model_path = "/home/user/app/political_ideology_model"
    return tf.keras.models.load_model(model_path)

# Function to load the tokenizer
def load_tokenizer():
    tokenizer_path = "/home/user/app/political_ideology_tokenizer"
    return AutoTokenizer.from_pretrained(tokenizer_path)

# Load model and tokenizer
model = load_model()
tokenizer = load_tokenizer()

# Mapping from numeric labels to text labels
id2label = {0: "Conservative", 1: "Liberal", 2: "Socialist", 3: "Libertarian"}

def predict_political_ideology(file_obj):
    try:
        # Decode the bytes object to get text
        text = file_obj.decode("utf-8")

        # Prepare input for the model
        inputs = tokenizer(text, truncation=True, padding=True, return_tensors="tf")
        model_output = model.predict({'input_ids': inputs["input_ids"], 'attention_mask': inputs["attention_mask"]})

        # Extract logits from the model's output
        logits = model_output['logits'] if isinstance(model_output, dict) else model_output

        # Convert logits to probabilities
        probabilities = tf.nn.softmax(logits, axis=-1)

        # Get the predicted class ID and its corresponding probability
        predicted_class_id = np.argmax(probabilities, axis=-1)[0]
        predicted_probability = float(probabilities[0, predicted_class_id])

        predicted_label = id2label[predicted_class_id]

        return f"{predicted_label} (Confidence: {predicted_probability:.2f})"
    except Exception as e:
        return str(e)  # Return the error for debugging


# Gradio Interface with file input
iface = gr.Interface(
    fn=predict_political_ideology,
    inputs=gr.File(type="binary", label="Upload Text File"),
    outputs="text",
    title="Political Ideology Predictor",
    description="Upload a text file to predict its political ideology."
)

# Launch the app
if __name__ == "__main__":
    iface.launch(inbrowser=True, share=True, auth=('User', 'Password'))