Change to FastAPI

app.py

@@ -1,37 +1,11 @@
-import streamlit as st
+from fastapi import FastAPI
 from transformers import pipeline
 
-# Initialize the zero-shot classification pipeline
-@st.cache_resource
-def load_classifier():
-    return pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
+app = FastAPI()
+classifier = pipeline("zero-shot-classification", model="facebook/bart-large-mnli")
 
-classifier = load_classifier()
-
-# Streamlit app interface
-st.title("Webpage Subject Classifier")
-st.write(
-    """
-    Enter the text content of a webpage below to classify it into one of the following subjects:
-    - Mathematics
-    - Language Arts
-    - Social Studies
-    - Science
-    """
-)
-
-# Text input area
-text_input = st.text_area("Paste the webpage content here:", height=200)
-
-# Define the candidate labels
-labels = ["Mathematics", "Language Arts", "Social Studies", "Science"]
-
-# Perform classification when the "Classify" button is clicked
-if st.button("Classify"):
-    if text_input.strip():
-        with st.spinner("Classifying..."):
-            result = classifier(text_input, labels)
-            predicted_label = result["labels"][0]
-            st.success(f"The predicted subject is: **{predicted_label}**")
-    else:
-        st.warning("Please enter some text to classify.")
+@app.post("/predict")
+async def predict(data: dict):
+    labels = ["Mathematics", "Language Arts", "Social Studies", "Science"]
+    result = classifier(data["text"], labels)
+    return {"label": result["labels"][0]}