Update app.py

app.py

@@ -6,6 +6,7 @@ import google.generativeai as genai
 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.status_code_pb2 import SUCCESS
+from clarifai_grpc.grpc.api.status import status_code_pb2
 from PIL import Image
 from io import BytesIO
 from nltk.tokenize import sent_tokenize
@@ -26,6 +27,12 @@ APP_ID_AUDIO = 'audio-generation'
 MODEL_ID_AUDIO = 'speech-synthesis'
 MODEL_VERSION_ID_AUDIO = 'f2cead3a965f4c419a61a4a9b501095c'
 
+# Renamed variables
+USER_ID_OBJECT = 'clarifai'
+APP_ID_OBJECT = 'main'
+MODEL_ID_OBJECT = 'general-image-detection'
+MODEL_VERSION_ID_OBJECT = '1580bb1932594c93b7e2e04456af7c6f'
+
 # Retrieve PAT from environment variable
 PAT = os.getenv('CLARIFAI_PAT')
 
@@ -45,6 +52,8 @@ GOOGLE_AI_STUDIO = os.environ.get('GOOGLE_API_KEY')
 
 # Story book
 
+# Image Creation +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
 # Function to generate image using Clarifai
 def generate_image(prompt):
     channel = ClarifaiChannel.get_grpc_channel()
@@ -69,6 +78,9 @@ def generate_image(prompt):
         image = Image.open(BytesIO(output))
         return image, None
 
+
+# Audio Creation +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+        
 # Function to generate audio using Clarifai
 def generate_audio(prompt):
     channel = ClarifaiChannel.get_grpc_channel()
@@ -92,6 +104,46 @@ def generate_audio(prompt):
         audio_output = response.outputs[0].data.audio.base64
         return audio_output, None
 
+
+# Object Detection +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
+
+
+# Function to call Clarifai API
+def get_image_concepts(image_bytes):
+    channel = ClarifaiChannel.get_grpc_channel()
+    stub = service_pb2_grpc.V2Stub(channel)
+
+   
+
+    metadata = (('authorization', 'Key ' + PAT),)
+    userDataObject = resources_pb2.UserAppIDSet(user_id=USER_ID_OBJECT, app_id=APP_ID_OBJECT)
+
+    post_model_outputs_response = stub.PostModelOutputs(
+        service_pb2.PostModelOutputsRequest(
+            user_app_id=userDataObject,
+            model_id=MODEL_ID_OBJECT,
+            version_id=MODEL_VERSION_ID_OBJECT,
+            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
+
+
+        
+
 # Function to split text into sentences and then chunk them
 
 def split_text_into_sentences_and_chunks(text, n=8):
@@ -364,6 +416,9 @@ with tab3:
                     options=list(range(len(st.session_state['image_paths']))),
                     format_func=lambda x: f"Image {x + 1}"
                 )
+
+                
+                
                 st.image(st.session_state['image_paths'][image_index])
         
                 # Button for actions related to the selected image