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import os
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 = os.getenv('CLARIFAI_PAT')
USER_ID = 'clarifai'
APP_ID = 'main'
MODEL_ID = 'general-image-detection'
MODEL_VERSION_ID = '1580bb1932594c93b7e2e04456af7c6f'
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`
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