app.py

import gradio as gr
import os
from dotenv import load_dotenv
from azure.ai.textanalytics import TextAnalyticsClient
from azure.core.credentials import AzureKeyCredential

# Load .env file
load_dotenv()

# this is the end point for the Azure service I created
azure_endpoint = os.getenv("AZURE_API_ENDPOINT")
# this is the KEY for the Azure API Service I created. 
azure_api_key = os.getenv("AZURE_API_KEY")

print(f"Azure API token: {azure_api_key}")

# call to the Azure TextAnalyticsClient API
text_analytics_client = TextAnalyticsClient(endpoint=azure_endpoint, credential=AzureKeyCredential(azure_api_key))

# function which take sthe inut text from the user , then make a call to Azure text analytics service and returns the output in json format with key putput parameter
def classify_text_azure(text):
    try:
        # Ensure input is in the correct format (list of strings)
        documents = [text]
        result = text_analytics_client.analyze_sentiment(documents=documents)
        
        # Format the response
        return [
            {
                "id": i,
                "sentiment": doc.sentiment,
                "confidence_scores": doc.confidence_scores
            } for i, doc in enumerate(result)
        ]
    except Exception as e:
        return {"error": str(e)}

def main():
    """
    Launch the Gradio interface for sentiment analysis.
    """
    # Define the Gradio interface
    interface = gr.Interface(
        fn=classify_text_azure,
        inputs=gr.Textbox(lines=2, placeholder="Enter Text Here..."),
        outputs="json",
        title="Text Classification with Azure Text Analytics",
        description="This interface uses Azure Text Analytics to classify text sentiments. Enter a sentence to see its classification."
    )

    # Launch the Gradio app
    interface.launch()

if __name__ == "__main__":
    main()