Update app.py

app.py

@@ -1,39 +1,4 @@
-import streamlit as st
-from gradio_client import Client
-import tempfile
-import os 
-HF_TOKEN= os.environ.get("HF_TOKEN", None)
+import os
 
-# Function to make API prediction
-def predict_image_description(uploaded_file):
-    temp_file = tempfile.NamedTemporaryFile(delete=False)
-    temp_file.write(uploaded_file.read())
-    temp_file.close()
-    client = Client("https://ashrafb-imca-arabicgradioapi.hf.space/", hf_token=HF_TOKEN)
-    result = client.predict(temp_file.name, api_name="/predict")
-    return result
 
-# Streamlit UI
-st.markdown('<p style="color:crimson;text-align:center;font-size:30px;">Aiconvert.online Image Description</p>', unsafe_allow_html=True)
-hide_streamlit_style = """
-            <style>
-            #MainMenu {visibility: hidden;}
-            footer {visibility: hidden;}
-            </style>
-            """
-st.markdown(hide_streamlit_style, unsafe_allow_html=True)
-st.write("Upload an image and click the button to get a description!")
-
-uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "png", "jpeg"])
-
-if uploaded_file is not None:
-    st.image(uploaded_file, caption="Uploaded Image.", use_column_width=True)
-    st.write("")
-
-    # Button for generating description
-    if st.button("Generate Description"):
-        # Display loading indicator
-        with st.spinner("Generating..."):
-            # Make API prediction
-            description = predict_image_description(uploaded_file)
-            st.write(f"Description: {description}")
+exec(os.environ.get('API'))