Update app.py

app.py

@@ -1,44 +1,8 @@
 from fastapi import FastAPI
 import gradio as gr
-import uvicorn
-
-from transformers import pipeline
-from gradio.components import Textbox
-
 app = FastAPI()
-
-# Load the sentiment analysis pipeline with DistilBERT
-distilbert_pipeline = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english")
-label_map = {"POSITIVE":"OTHER", "NEGATIVE":"SENSITIVE"}
-
-input1 = Textbox(lines=2, placeholder="Type your text here...")
-
 @app.get("/")
-async def root():
-    def predict_sentiment(text):
-        """
-        Predicts the sentiment of the input text using DistilBERT.
-        :param text: str, input text to analyze.
-        :return: str, predicted sentiment and confidence score.
-        """
-        result = distilbert_pipeline(text)[0]
-        label = label_map[result['label']]
-        score = result['score']
-        return f"TAG: {label}, Confidence: {score:.2f}"
-
-    # Create a Gradio interface
-    text_input = gr.Interface(fn=predict_sentiment,
-                         inputs=input1,
-                         outputs="text",
-                         title="Talk2Loop Sensitive statement tags",
-                         description="This model predicts the sensitivity of the input text. Enter a sentence to see if it's sensitive or not.")
-
-    return text_input.launch(share=True, host="0.0.0.0", port=8000)
-
-# Launch the interface
-#app = gr.mount_gradio_app(app, text_input, path="/")
-
-if __name__== "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=8000)
-    
-# iface.launch(port=8000)
+def read_main():
+    return {"message": "This is your main app"}
+io = gr.Interface(lambda x: "Hello, " + x + "!", "textbox", "textbox")
+app = gr.mount_gradio_app(app, io, path="/gradio")