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temp_uploaded_image.*
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temp_uploaded_video.*

ai/generative-ai-service/decode-Images-and-Videos-with-OCI-GenAI/README.md

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<img src="./image.png">
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</img>
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Reviewed date: 22.09.2025
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Reviewed date: 09.10.2025
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## Features
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## Installation
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1. **Clone the repository:**
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1. **Clone this repository.**
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2. **Install dependencies:**
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Make sure you have Python 3.8+ installed. Then, install the required libraries:
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3. **Configure OCI:**
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- Set up your OCI configuration by creating or updating the `~/.oci/config` file with your credentials and profile.
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- Replace placeholders like `compartmentId`, `llm_service_endpoint`, and `visionModel` in the code with your actual values.
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- Replace placeholders like `compartmentId`, `llm_service_endpoint`, and `visionModel` in [`app.py`](./app.py) with your actual values.
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---
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ai/generative-ai-service/decode-Images-and-Videos-with-OCI-GenAI/app.py

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@@ -61,7 +61,6 @@ def get_chat_request(encoded_image=None, user_prompt=None):
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return chat_request
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def cohere_chat_request(encoded_image=None, user_prompt=None):
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print(" i am here")
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chat_request = oci.generative_ai_inference.models.CohereChatRequest()
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chat_request.api_format = oci.generative_ai_inference.models.BaseChatRequest.API_FORMAT_COHERE
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message = get_message(encoded_image, user_prompt)
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while success:
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if count % (frame_rate * interval) == 0:
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frames.append(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
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frames.append(frame)
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success, frame = cap.read()
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count += 1
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cap.release()
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streamlit==1.37.0
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oci==3.50.1
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oci>=2.161.0,<3
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Pillow
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opencv-python-headless==4.10.0.84
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tqdm==4.66.3
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tqdm==4.66.3

ai/generative-ai-service/image-to-text/README.md

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This application is built using **Streamlit** and **Oracle OCI Generative AI**, allowing users to upload an image, input a prompt, and receive a text-based response generated by the AI model. It leverages Oracle's Gen AI Inference API for processing multimodal data (text and image).
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Reviewed: 22.09.2025
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Reviewed: 09.10.2025
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<img src="./image1.png">
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</img>
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## Installation
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1. Clone the repository:
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1. Clone this repository.
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2. Install dependencies:
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```bash
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3. Configure OCI:
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Ensure your `~/.oci/config` file is set up with the correct credentials and profile.
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4. Edit [`app.py`](./app.py) and insert a valid Compartment OCID.
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## Usage

ai/generative-ai-service/image-to-text/app.py

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llm_service_endpoint = "https://inference.generativeai.us-chicago-1.oci.oraclecloud.com"
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# Define functions
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def encode_image(image_path):
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with open(image_path, "rb") as image_file:
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return base64.b64encode(image_file.read()).decode("utf-8")
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def get_message(encoded_image, user_prompt):
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content1 = oci.generative_ai_inference.models.TextContent()
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content1.text = user_prompt
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user_prompt = st.text_input("Enter your prompt for the image:", value="Tell me about this image.")
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if uploaded_file:
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# Save the uploaded image temporarily
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temp_image_path = "temp_uploaded_image.jpg"
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with open(temp_image_path, "wb") as f:
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f.write(uploaded_file.getbuffer())
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# Display the uploaded image
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st.image(temp_image_path, caption="Uploaded Image", use_column_width=True)
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st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
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# Process and call the model
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if st.button("Generate Response"):
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with st.spinner("Please wait while the model processes the image..."):
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try:
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# Encode the image
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encoded_image = encode_image(temp_image_path)
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encoded_image = base64.b64encode(uploaded_file.getvalue()).decode("utf-8")
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# Setup OCI client
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CONFIG_PROFILE = "DEFAULT"
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streamlit==1.37.0
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oci==3.50.1
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Pillow
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oci>=2.161.0,<3
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Pillow

app-dev/devops-and-containers/oke/oke-gitops/README.md

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* Create 2 OCI Code Repositories: one with pipelines definitions, and another one called "oke-cluster-config" with the git template for the OKE cluster administrators
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* Create an OCI Build Pipeline that will mirror the ArgoCD Helm Chart inside the Oracle Cloud Registry, and deploy it in the chosen cluster
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[![Deploy to Oracle Cloud](https://oci-resourcemanager-plugin.plugins.oci.oraclecloud.com/latest/deploy-to-oracle-cloud.svg)](https://cloud.oracle.com/resourcemanager/stacks/create?zipUrl=https://github.com/oracle-devrel/technology-engineering/releases/download/oke-gitops-1.1.0/stack.zip)
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[![Deploy to Oracle Cloud](https://oci-resourcemanager-plugin.plugins.oci.oraclecloud.com/latest/deploy-to-oracle-cloud.svg)](https://cloud.oracle.com/resourcemanager/stacks/create?zipUrl=https://github.com/oracle-devrel/technology-engineering/releases/download/oke-gitops-1.1.1/stack.zip)
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Once the stack has been provisioned, you can modify the ArgoCD version to deploy by editing the `mirror_argo.yaml` file in the `pipelines` repository.
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By default, ArgoCD will be deployed in an "insecure" mode to disable the default SSL certificate, but feel free to modify the chart values in the `argo-cd-chart-values` artifact.
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{
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"chart": {
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"repo": "https://example-app/chart",
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"name": "app",
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"version": "*",
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"release-name": "example-app"
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},
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"namespace": "default"
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}
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{
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"chart": {
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"repo": "https://example-app/chart",
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"name": "app",
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"version": "1.0.0",
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"release-name": "example-app"
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},
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"namespace": "default"
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}

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