anshuman image to text code

Author: jesusbrasero

ai/generative-ai-service/image-to-text/LICENSE

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+Copyright (c) 2024 Oracle and/or its affiliates.
+
+The Universal Permissive License (UPL), Version 1.0
+
+Subject to the condition set forth below, permission is hereby granted to any
+person obtaining a copy of this software, associated documentation and/or data
+(collectively the "Software"), free of charge and under any and all copyright
+rights in the Software, and any and all patent rights owned or freely
+licensable by each licensor hereunder covering either (i) the unmodified
+Software as contributed to or provided by such licensor, or (ii) the Larger
+Works (as defined below), to deal in both
+
+(a) the Software, and
+(b) any piece of software and/or hardware listed in the lrgrwrks.txt file if
+one is included with the Software (each a "Larger Work" to which the Software
+is contributed by such licensors),
+
+without restriction, including without limitation the rights to copy, create
+derivative works of, display, perform, and distribute the Software and make,
+use, sell, offer for sale, import, export, have made, and have sold the
+Software and the Larger Work(s), and to sublicense the foregoing rights on
+either these or other terms.
+
+This license is subject to the following condition:
+The above copyright notice and either this complete permission notice or at
+a minimum a reference to the UPL must be included in all copies or
+substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

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

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+
+# Image-to-Text with Oracle OCI Gen AI
+
+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).
+
+Reviewed: 19.11.2024
+
+<img src="./image1.png">
+</img>
+
+---
+
+## Features
+
+- Upload an image file (`.png`, `.jpg`, `.jpeg`).
+- Provide a natural language prompt describing your query about the image.
+- Get a detailed response generated by Oracle's Generative AI model.
+- Easy-to-use interface built with Streamlit.
+
+---
+
+## Prerequisites
+
+1. **Oracle OCI Configuration**
+   - Set up your Oracle Cloud Infrastructure (OCI) account.
+   - Obtain the following:
+     - **Compartment OCID**
+     - **Generative AI Service Endpoint**
+     - **Model ID** (e.g., `meta.llama-3.2-90b-vision-instruct`).
+   - Configure your `~/.oci/config` file with your profile details.
+
+2. **Python Environment**
+   - Install Python 3.8 or later.
+   - Install required dependencies (see below).
+
+---
+
+## Installation
+
+1. Clone the repository:
+  
+
+2. Install dependencies:
+   ```bash
+   pip install -r requirements.txt
+   ```
+
+3. Configure OCI:
+   Ensure your `~/.oci/config` file is set up with the correct credentials and profile.
+
+---
+
+## Usage
+
+1. Run the application:
+   ```bash
+   streamlit run app.py
+   ```
+
+2. Open the web application in your browser at `http://localhost:8501`.
+
+3. Upload an image and provide a prompt in the text input field. Click **Generate Response** to receive the AI-generated output.
+
+---
+
+## File Structure
+
+```plaintext
+.
+├── app.py                  # Main application file
+├── requirements.txt        # Python dependencies
+└── README.md               # Project documentation
+```
+
+---
+
+## Dependencies
+
+List of dependencies (found in `requirements.txt`):
+- **Streamlit**: For creating the web UI.
+- **oci**: Oracle Cloud Infrastructure SDK.
+- **base64**: For encoding images.
+
+Install them using:
+```bash
+pip install -r requirements.txt
+```
+
+---
+
+## Notes
+
+- Ensure your OCI credentials and Compartment OCID are correct in the script.
+- Check the image format and size for compatibility.
+- Use the appropriate Generative AI service endpoint for your region.
+
+---
+
+## Troubleshooting
+
+- **Error: `oci.exceptions.ServiceError`**
+  - Check if your compartment OCID and API keys are configured correctly.
+  
+- **Streamlit does not load:**
+  - Verify that Streamlit is installed and the application is running on the correct port.
+
+
+
+---
+
+## Acknowledgments
+
+- [Oracle Cloud Infrastructure (OCI)](https://www.oracle.com/cloud/)
+- [Streamlit Documentation](https://docs.streamlit.io/)
+  
+For questions or feedback, please contact [[email protected]].

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

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+# Author: Ansh
+import streamlit as st
+import oci
+import base64
+from PIL import Image
+
+# OCI Configuration ( Put your compartment id below)
+compartmentId = "ocid1.compartment.oc1..***************************"
+llm_service_endpoint = "https://inference.generativeai.us-chicago-1.oci.oraclecloud.com"
+
+# Define functions
+def encode_image(image_path):
+    with open(image_path, "rb") as image_file:
+        return base64.b64encode(image_file.read()).decode("utf-8")
+
+def get_message(encoded_image, user_prompt):
+    content1 = oci.generative_ai_inference.models.TextContent()
+    content1.text = user_prompt
+    
+    content2 = oci.generative_ai_inference.models.ImageContent()
+    image_url = oci.generative_ai_inference.models.ImageUrl()
+    image_url.url = f"data:image/jpeg;base64,{encoded_image}"
+    content2.image_url = image_url
+    
+    message = oci.generative_ai_inference.models.UserMessage()
+    message.content = [content1, content2]
+    return message
+
+def get_chat_request(encoded_image, user_prompt):
+    chat_request = oci.generative_ai_inference.models.GenericChatRequest()
+    chat_request.messages = [get_message(encoded_image, user_prompt)]
+    chat_request.api_format = oci.generative_ai_inference.models.BaseChatRequest.API_FORMAT_GENERIC
+    chat_request.num_generations = 1
+    chat_request.is_stream = False
+    chat_request.max_tokens = 500
+    chat_request.temperature = 0.75
+    chat_request.top_p = 0.7
+    chat_request.top_k = -1
+    chat_request.frequency_penalty = 1.0
+    return chat_request
+
+def get_chat_detail(chat_request):
+    chat_detail = oci.generative_ai_inference.models.ChatDetails()
+    chat_detail.serving_mode = oci.generative_ai_inference.models.OnDemandServingMode(model_id="meta.llama-3.2-90b-vision-instruct")
+    chat_detail.compartment_id = compartmentId
+    chat_detail.chat_request = chat_request
+    return chat_detail
+
+# Streamlit UI
+st.title("Image to Text with Oci Gen AI")
+st.write("Upload an image, provide a prompt, and get a response from Oci Gen AI.")
+
+# Upload image
+uploaded_file = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"])
+
+# Prompt input
+user_prompt = st.text_input("Enter your prompt for the image:", value="Tell me about this image.")
+
+if uploaded_file:
+    # Save the uploaded image temporarily
+    temp_image_path = "temp_uploaded_image.jpg"
+    with open(temp_image_path, "wb") as f:
+        f.write(uploaded_file.getbuffer())
+    
+    # Display the uploaded image
+    st.image(temp_image_path, caption="Uploaded Image", use_column_width=True)
+    
+    # Process and call the model
+    if st.button("Generate Response"):
+        with st.spinner("Please wait while the model processes the image..."):
+            try:
+                # Encode the image
+                encoded_image = encode_image(temp_image_path)
+                
+                # Setup OCI client
+                CONFIG_PROFILE = "DEFAULT"
+                config = oci.config.from_file('~/.oci/config', CONFIG_PROFILE)
+                
+                llm_client = oci.generative_ai_inference.GenerativeAiInferenceClient(
+                    config=config,
+                    service_endpoint=llm_service_endpoint,
+                    retry_strategy=oci.retry.NoneRetryStrategy(),
+                    timeout=(10, 240)
+                )
+                
+                # Get the chat request and response
+                llm_request = get_chat_request(encoded_image, user_prompt)
+                llm_payload = get_chat_detail(llm_request)
+                llm_response = llm_client.chat(llm_payload)
+                
+                # Extract and display the response
+                llm_text = llm_response.data.chat_response.choices[0].message.content[0].text
+                st.success("Model Response:")
+                st.write(llm_text)
+            except Exception as e:
+                st.error(f"An error occurred: {str(e)}")

ai/generative-ai-service/image-to-text/image1.png

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+streamlit==1.33.0
+oci==3.50.1
+Pillow