app.py

import gradio as gr
from transformers import pipeline
from sklearn.metrics import accuracy_score, precision_recall_fscore_support, confusion_matrix, roc_curve, auc
import matplotlib.pyplot as plt
import numpy as np

# Initialize the sentiment analysis pipeline with a multilingual model
sentiment_analysis = pipeline("sentiment-analysis", model="bert-base-multilingual-cased")

def analyze_sentiment(text):
    result = sentiment_analysis(text)
    return result[0]

# Mock functions to calculate metrics - Replace with actual implementation
def calculate_metrics(y_true, y_pred):
    accuracy = accuracy_score(y_true, y_pred)
    precision, recall, f1, _ = precision_recall_fscore_support(y_true, y_pred, average='binary')
    cm = confusion_matrix(y_true, y_pred)
    fpr, tpr, _ = roc_curve(y_true, y_pred)
    roc_auc = auc(fpr, tpr)
    return accuracy, precision, recall, f1, cm, fpr, tpr, roc_auc

def plot_confusion_matrix(cm):
    # Plot confusion matrix here
    pass

def plot_roc_curve(fpr, tpr, roc_auc):
    # Plot ROC curve here
    pass

# Replace this with actual test data and predictions
y_true = [0, 1, 0, 1]  # True labels
y_pred = [0, 1, 0, 1]  # Predicted labels

# Calculate metrics
accuracy, precision, recall, f1, cm, fpr, tpr, roc_auc = calculate_metrics(y_true, y_pred)

# Plot confusion matrix and ROC curve
plot_confusion_matrix(cm)
plot_roc_curve(fpr, tpr, roc_auc)

# Create a Gradio interface
interface = gr.Interface(
    fn=analyze_sentiment,
    inputs=gr.inputs.Textbox(lines=2, placeholder="Enter Text Here..."),
    outputs="text"
)

interface.launch()