app update

app.py

@@ -1,19 +1,21 @@
 import gradio as gr
-from fastapi import FastAPI
-import uvicorn
+from transformers import pipeline
 
-# Create a FastAPI app
-app = FastAPI()
+# Load the BERT-Emotions-Classifier model
+classifier = pipeline("text-classification", model="ayoubkirouane/BERT-Emotions-Classifier")
 
-# Create Gradio interface (just like in Hugging Face Spaces)
+# Define the prediction function for emotion classification
 def classify_emotion(text):
-    # Your model code here (replace this with the actual classification)
-    return "happy"  # example return value
+    result = classifier(text)
+    return result[0]['label'], result[0]['score']
 
-gradio_interface = gr.Interface(fn=classify_emotion, inputs="text", outputs="text")
+# Define the Gradio interface
+iface = gr.Interface(
+    fn=classify_emotion,          # function that will classify emotion
+    inputs=gr.Textbox(),          # input text box
+    outputs=[gr.Textbox(), gr.Textbox()],  # output emotion label and score
+    live=True                      # Enable live mode (optional)
+)
 
-# Mount the Gradio app inside FastAPI
-app.mount("/gradio", gradio_interface)
-
-if __name__ == "__main__":
-    uvicorn.run(app, host="0.0.0.0", port=7860)
+# Launch the Gradio interface as an API
+iface.launch(share=True)