req update

app.py

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

requirements.txt

@@ -1,3 +1,4 @@
 transformers
 gradio
-torch
+torch
+uvicorn