Update app.py

app.py

@@ -1,5 +1,6 @@
 import os
-import gradio as gr
+from fastapi import FastAPI, HTTPException
+from pydantic import BaseModel
 from transformers import AutoTokenizer, AutoModelForSequenceClassification
 import torch
 import warnings
@@ -19,6 +20,11 @@ model = AutoModelForSequenceClassification.from_pretrained(MODEL_NAME)
 # Move model to appropriate device
 model = model.to(device)
 
+app = FastAPI(title="Formality Classifier API")
+
+class TextInput(BaseModel):
+    text: str
+
 def calculate_formality_percentages(score):
     # Convert score to grayscale percentage (0-100)
     grayscale = int(score * 100)
@@ -27,10 +33,15 @@ def calculate_formality_percentages(score):
     informal_percent = 100 - grayscale
     return formal_percent, informal_percent
 
-def predict_formality(text):
+@app.get("/")
+async def home():
+    return {"message": "Formality Classifier API is running! Use /predict to classify text."}
+
+@app.post("/predict")
+async def predict_formality(input_data: TextInput):
     try:
         # Tokenize input
-        encoding = tokenizer(text, return_tensors="pt", truncation=True, padding=True)
+        encoding = tokenizer(input_data.text, return_tensors="pt", truncation=True, padding=True)
         encoding = {k: v.to(device) for k, v in encoding.items()}
 
         # Predict formality score
@@ -51,32 +62,8 @@ def predict_formality(text):
         
         return response
     except Exception as e:
-        return {
-            "error": str(e),
-            "formality_score": 0,
-            "formal_percent": 0,
-            "informal_percent": 0,
-            "classification": "Error processing the text."
-        }
-
-# Create Gradio interface
-demo = gr.Interface(
-    fn=predict_formality,
-    inputs=gr.Textbox(label="Enter your text", lines=3),
-    outputs=gr.JSON(label="Formality Analysis"),
-    title="Formality Classifier",
-    description="Enter text to analyze its formality level.",
-    examples=[
-        ["Hello, how are you doing today?"],
-        ["Hey, what's up?"],
-        ["I would like to request your assistance with this matter."]
-    ]
-)
+        raise HTTPException(status_code=500, detail=str(e))
 
-# Launch the app
 if __name__ == "__main__":
-    demo.queue()  # Enable request queuing
-    demo.launch(
-        server_name="0.0.0.0",
-        server_port=7860
-    )
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=7860)