Update app.py

app.py

@@ -1,36 +1,59 @@
 import os
-from fastapi import FastAPI, Request
-from fastapi.responses import JSONResponse
+import gradio as gr
 from transformers import pipeline
 
-app = FastAPI()
-
 token = os.environ.get("HF_TOKEN")
 
 english_model = None
 urdu_model = None
 
+
 def get_english_model():
     global english_model
     if english_model is None:
-        english_model = pipeline("text-classification", model="mrgmd01/Finetuned_siebert-sentiment-roberta-large-english", token=token)
+        english_model = pipeline(
+            "text-classification",
+            model="mrgmd01/Finetuned_siebert-sentiment-roberta-large-english",
+            token=token
+        )
     return english_model
 
+
 def get_urdu_model():
     global urdu_model
     if urdu_model is None:
-        urdu_model = pipeline("text-classification", model="mrgmd01/SA_Model_bert-base-multilingual-uncased", token=token)
+        urdu_model = pipeline(
+            "text-classification",
+            model="mrgmd01/SA_Model_bert-base-multilingual-uncased",
+            token=token
+        )
     return urdu_model
 
-@app.post("/predict")
-async def predict(request: Request):
-    body = await request.json()
-    text = body.get("text", "")
-    language = body.get("language", "english")
-    
+
+def predict(text, language):
     if language in ["urdu", "roman_urdu"]:
         result = get_urdu_model()(text)[0]
     else:
         result = get_english_model()(text)[0]
-    
-    return JSONResponse({"label": result["label"], "score": result["score"]})
+
+    return {
+        "label": result["label"],
+        "score": float(result["score"])
+    }
+
+
+iface = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Textbox(label="Input Text"),
+        gr.Dropdown(
+            choices=["english", "urdu", "roman_urdu"],
+            value="english",
+            label="Language"
+        )
+    ],
+    outputs="json",
+    title="Multilingual Sentiment Analysis"
+)
+
+iface.launch()