added code

main.py

@@ -1,21 +1,28 @@
-import gradio as gr
-import pandas as pd
+from fastapi import FastAPI, HTTPException
+from fastapi.responses import HTMLResponse
+import matplotlib.pyplot as plt
+from io import BytesIO
+import base64
+from gradio_client import Client
 
-simple = pd.DataFrame(
-    {
-        "item": ["A", "B", "C", "D", "E", "F", "G", "H", "I"],
-        "inventory": [28, 55, 43, 91, 81, 53, 19, 87, 52],
-    }
-)
+app = FastAPI()
 
-with gr.Blocks() as demo:
-    gr.BarPlot(
-        value=simple,
-        x="item",
-        y="inventory",
-        title="Simple Bar Plot",
-        container=False,
+@app.post("/predict")
+async def predict(
+    name1: str, score1: int,
+    name2: str, score2: int,
+    name3: str, score3: int,
+    name4: str, score4: int,
+    name5: str, score5: int
+):
+    # Create a Gradio client and make a prediction
+    gradio_client = Client("https://deepak6593-gradio-app.hf.space/--replicas/9890g/")
+    result = gradio_client.predict(
+        name1, score1, name2, score2, name3, score3, name4, score4, name5, score5,
+        api_name="/predict"
     )
 
-if __name__ == "__main__":
-    demo.launch(share=True)
+    # HTML response containing the iframe
+    iframe_html = f'<iframe src="{result['plot']}" width="600" height="400"></iframe>'
+    
+    return HTMLResponse(content=iframe_html)