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| import os | |
| import base64 | |
| import requests | |
| import streamlit as st | |
| import json | |
| if "stream" not in st.session_state: | |
| st.session_state.stream = True | |
| api_key = os.getenv("NVIDIA_VISION_API_KEY") | |
| MODEL_ID = "meta/llama-3.2-90b-vision-instruct" | |
| invoke_url = "https://ai.api.nvidia.com/v1/gr/meta/llama-3.2-90b-vision-instruct/chat/completions" | |
| # Function to encode the image | |
| def encode_image(image_path): | |
| with open(image_path, "rb") as image_file: | |
| return base64.b64encode(image_file.read()).decode('utf-8') | |
| def extract_content(chunk): | |
| try: | |
| decoded_chunk = chunk.decode('utf-8') | |
| json_data = decoded_chunk.split('data: ')[1] | |
| parsed_data = json.loads(json_data) | |
| content = parsed_data['choices'][0]['delta']['content'] | |
| return content | |
| except json.JSONDecodeError as e: | |
| #ignore the error | |
| return "" | |
| def main(): | |
| st.title("Multimodal Image Analysis with " + MODEL_ID) | |
| text = """Prof. Louie F. Cervantes, M. Eng. (Information Engineering) | |
| CCS 229 - Intelligent Systems | |
| Department of Computer Science | |
| College of Information and Communications Technology | |
| West Visayas State University | |
| """ | |
| with st.expander("About"): | |
| st.text(text) | |
| st.write("Upload an image and select the image analysis task.") | |
| # File upload for image | |
| uploaded_image = st.file_uploader("Upload an Image", type=["jpg", "jpeg", "png"]) | |
| if uploaded_image is not None: | |
| # Encode the uploaded image to base64 | |
| base64_image = base64.b64encode(uploaded_image.getvalue()).decode('utf-8') | |
| # Display the uploaded image | |
| st.image(uploaded_image, caption="Uploaded Image", use_container_width=True) | |
| # List of image analysis tasks | |
| analysis_tasks = [ | |
| "Scene Analysis: Describe the scene depicted in the image. Identify the objects present, their spatial relationships, and any actions taking place.", | |
| "Object Detection and Classification: Identify and classify all objects present in the image. Provide detailed descriptions of each object, including its size, shape, color, and texture.", | |
| "Image Captioning: Generate a concise and accurate caption that describes the content of the image.", | |
| "Visual Question Answering: Answer specific questions about the image, such as 'What color is the car?' or 'How many people are in the image?'", | |
| "Image Similarity Search: Given a query image, find similar images from a large dataset based on visual features.", | |
| "Image Segmentation: Segment the image into different regions corresponding to objects or areas of interest.", | |
| "Optical Character Recognition (OCR): Extract text from the image, such as printed or handwritten text.", | |
| "Diagram Understanding: Analyze a diagram (e.g., flowchart, circuit diagram) and extract its structure and meaning.", | |
| "Art Analysis: Describe the artistic style, subject matter, and emotional impact of an image.", | |
| "Medical Image Analysis: Analyze medical images (e.g., X-rays, MRIs) to detect abnormalities or diagnose diseases." | |
| ] | |
| # Task selection dropdown | |
| selected_task = st.selectbox("Select an image analysis task:", analysis_tasks) | |
| if st.button("Generate Response"): | |
| st.session_state.stream = st.checkbox("Begin streaming the AI response as soon as it is available.", value=True) | |
| stream = st.session_state.stream | |
| if uploaded_image is None or selected_task == "": | |
| st.error("Please upload an image and select a task.") | |
| return | |
| else: | |
| headers = { | |
| "Authorization": f"Bearer {api_key}", | |
| "Accept": "text/event-stream" if stream else "application/json" | |
| } | |
| # Prepare the multimodal prompt | |
| payload = { | |
| "model": MODEL_ID, | |
| "messages": [ | |
| { | |
| "role": "user", | |
| "content": f'{selected_task} <img src="data:image/png;base64,{base64_image}" />' | |
| } | |
| ], | |
| "max_tokens": 512, | |
| "temperature": 1.00, | |
| "top_p": 1.00, | |
| "stream": stream | |
| } | |
| with st.spinner("Processing..."): | |
| response = requests.post( | |
| invoke_url, | |
| headers=headers, | |
| json=payload, | |
| stream=stream # Important for streaming | |
| ) | |
| if stream: | |
| response_container = st.empty() | |
| content = "" | |
| # Efficiently handle streaming response | |
| for chunk in response.iter_lines(): | |
| if len(chunk) > 0: | |
| # Decode the bytes object into a string | |
| chunk_str = chunk.decode('utf-8') | |
| # Remove the "data: " prefix | |
| if chunk_str.startswith("data: "): | |
| chunk_str = chunk_str[6:] | |
| if chunk_str.strip() == "[DONE]": | |
| break | |
| # Check if the string is not empty | |
| if chunk_str.strip() != "": | |
| try: | |
| # Attempt to parse the string as JSON | |
| chunk_dict = json.loads(chunk_str) | |
| # Now you can access the 'choices' key | |
| content += chunk_dict['choices'][0]['delta']['content'] | |
| response_container.markdown(content) | |
| except json.JSONDecodeError as e: | |
| # Handle the error if the string is not valid JSON | |
| print(f"Error parsing JSON: {e}") | |
| print(f"Invalid JSON string: {chunk_str}") | |
| else: | |
| try: | |
| content = response.json() | |
| content_string = content.get('choices', [{}])[0].get('message', {}).get('content', '') | |
| st.write(f"AI Response: {content_string}") | |
| st.success("Response generated!") | |
| except Exception as e: | |
| st.error(f"An error occurred: {e}") | |
| if __name__ == "__main__": | |
| main() |