Update app.py

app.py

@@ -1,28 +1,27 @@
-# streamlit_app/app.py
-
-import streamlit as st
-import requests
-from PIL import Image
-
-# Define FastAPI backend URL
-backend_url = "http://localhost:7384"
-
-def main():
-    st.title("SETI Signals Classifier")
-
-    # Example: Upload file and send POST request to FastAPI endpoint
-    uploaded_file = st.file_uploader("Choose an image to predict...", type=["png"])
-    st.markdown("You can get sample images from [here](https://github.com/bhuvaneshprasad/End-to-End-SETI-Classification-using-CNN-MLFlow-DVC/tree/main/assets/test_images) to predict.")
-    if uploaded_file is not None:
-        with st.spinner('Predicting...'):
-            files = {"file": uploaded_file}
-            response = requests.post(f"{backend_url}/predict", files=files)
-            if response.status_code == 200:
-                st.json(response.json())
-                image = Image.open(uploaded_file)
-                st.image(image, caption="Uploaded Image", use_column_width=True)
-            else:
-                st.error("Failed to predict")
-
-if __name__ == "__main__":
-    main()
+# streamlit_app/app.py
+from prediction import PredictionPipeline
+import streamlit as st
+import requests
+from PIL import Image
+
+def main():
+    st.title("SETI Signals Classifier")
+
+    # Example: Upload file and send POST request to FastAPI endpoint
+    uploaded_file = st.file_uploader("Choose an image to predict...", type=["png"])
+    st.markdown("You can get sample images from [here](https://github.com/bhuvaneshprasad/End-to-End-SETI-Classification-using-CNN-MLFlow-DVC/tree/main/assets/test_images) to predict.")
+    if uploaded_file is not None:
+        with st.spinner('Predicting...'):
+            files = {"file": uploaded_file}
+            predictor = PredictionPipeline(files)
+            prediction = predictor.predict()
+            st.title(type(prediction))
+            if type(prediction) == str:
+                st.json({'prediction' : prediction})
+                image = Image.open(uploaded_file)
+                st.image(image, caption="Uploaded Image", use_column_width=True)
+            else:
+                st.error("Failed to predict")
+
+if __name__ == "__main__":
+    main()