Create app.py

app.py

@@ -0,0 +1,58 @@
+import streamlit as st
+from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel
+from clarifai_grpc.grpc.api import resources_pb2, service_pb2, service_pb2_grpc
+from clarifai_grpc.grpc.api.status import status_code_pb2
+import io
+
+# Function to call Clarifai API
+def get_image_concepts(image_bytes):
+    channel = ClarifaiChannel.get_grpc_channel()
+    stub = service_pb2_grpc.V2Stub(channel)
+
+    PAT = 'YOUR_PERSONAL_ACCESS_TOKEN'
+    USER_ID = 'clarifai'
+    APP_ID = 'main'
+    MODEL_ID = 'general-image-detection'
+    MODEL_VERSION_ID = 'YOUR_MODEL_VERSION_ID'
+
+    metadata = (('authorization', 'Key ' + PAT),)
+    userDataObject = resources_pb2.UserAppIDSet(user_id=USER_ID, app_id=APP_ID)
+
+    post_model_outputs_response = stub.PostModelOutputs(
+        service_pb2.PostModelOutputsRequest(
+            user_app_id=userDataObject,
+            model_id=MODEL_ID,
+            version_id=MODEL_VERSION_ID,
+            inputs=[
+                resources_pb2.Input(
+                    data=resources_pb2.Data(
+                        image=resources_pb2.Image(
+                            base64=image_bytes
+                        )
+                    )
+                )
+            ]
+        ),
+        metadata=metadata
+    )
+
+    if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
+        raise Exception("Post model outputs failed, status: " + post_model_outputs_response.status.description)
+
+    return post_model_outputs_response.outputs[0].data.regions
+
+# Streamlit interface
+st.title("Image Detection with Clarifai")
+
+uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png"])
+if uploaded_file is not None:
+    image_bytes = uploaded_file.getvalue()
+    regions = get_image_concepts(image_bytes)
+    for region in regions:
+        # Display each detected item
+        for concept in region.data.concepts:
+            name = concept.name
+            value = round(concept.value, 4)
+            st.write(f"{name}: {value}")
+
+# Run this with `streamlit run your_script_name.py`