Update app.py

app.py

@@ -1,43 +1,41 @@
 import gradio as gr
 from azure.ai.textanalytics import TextAnalyticsClient
 from azure.core.credentials import AzureKeyCredential
-from transformers import pipeline
 
 # Azure Text Analytics setup
 azure_endpoint = "https://t6langservice.cognitiveservices.azure.com/"
-azure_api_key = "3CU1gpUszvl7pLb90Ivdp1Kd5WZc56savzdXOK5GV40JHebnxnxoJQQJ99BAACYeBjFXJ3w3AAAaACOGkRT5"
-# Authenticate client
+azure_api_key = "your-azure-api-key"  # Replace with your actual API key
 text_analytics_client = TextAnalyticsClient(endpoint=azure_endpoint, credential=AzureKeyCredential(azure_api_key))
 
-def load_model():
-    # Load a pre-trained HuggingFace pipeline for sentiment analysis
-    model_pipeline = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
-    return model_pipeline
+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 classify_text(model, text):
-    # Use the loaded model to classify text
-    result = model(text)
-    return result
-
-
-def classify_text_azure(model, text):
-    # Ensure input is in the correct format (list of strings)
-    documents = [text]  # Wrap the input string in a list
-    result = text_analytics_client.analyze_sentiment(documents=documents)
-    return [{"id": i, "sentiment": doc.sentiment, "confidence_scores": doc.confidence_scores} for i, doc in enumerate(result)]
-
-    
 def main():
-    # Load the model
-    model = load_model()
-
+    """
+    Launch the Gradio interface for sentiment analysis.
+    """
     # Define the Gradio interface
     interface = gr.Interface(
-        fn=lambda text: classify_text_azure(model, text),
+        fn=classify_text_azure,
         inputs=gr.Textbox(lines=2, placeholder="Enter Text Here..."),
         outputs="json",
-        title="Text Classification with HuggingFace",
-        description="This interface uses a HuggingFace model to classify text sentiments. Enter a sentence to see its classification."
+        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