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| import streamlit as st | |
| import base64 | |
| import requests | |
| from PIL import Image | |
| import io | |
| def analyze_image_ui(): | |
| """ | |
| Renders the UI for the Viz AI Image Analysis tool and handles the | |
| logic for sending requests to the OpenRouter API. | |
| """ | |
| st.markdown("---") | |
| st.markdown("<h2 style='text-align: center; color: #4A90E2;'>🤖 Viz AI (Image)</h2>", unsafe_allow_html=True) | |
| st.markdown("<h5 style='text-align: center; color: grey;'>Uncover hidden patterns and details in your images.</h5>", unsafe_allow_html=True) | |
| # Use a two-column layout | |
| col1, col2 = st.columns(2) | |
| with col1: | |
| uploaded_image = st.file_uploader("Upload an image", type=["png", "jpg", "jpeg"]) | |
| if uploaded_image: | |
| # Display the uploaded image | |
| image = Image.open(uploaded_image) | |
| st.image(image, caption="Uploaded Image", use_container_width=True) | |
| with col2: | |
| prompt_text = st.text_area( | |
| "Your Prompt:", | |
| "Describe this image in detail. What are the key objects, arrangements, and potential hidden patterns or meanings?", | |
| height=150 | |
| ) | |
| # Add a select box for the model | |
| model_selection = st.selectbox( | |
| "Choose a model:", | |
| ( | |
| "meta-llama/llama-4-maverick:free", | |
| "opengvlab/internvl3-14b:free", | |
| "mistralai/mistral-small-3.1-24b-instruct:free", | |
| "google/gemma-3-27b-it:free", | |
| ) | |
| ) | |
| analyze_button = st.button("Analyze Image ✨") | |
| if analyze_button and uploaded_image: | |
| if not prompt_text.strip(): | |
| st.error("Please enter a prompt.") | |
| return | |
| with st.spinner(f"AI is analyzing the image using {model_selection}..."): | |
| try: | |
| # Get the API key from secrets | |
| api_key = st.secrets["OPENROUTER_API_KEY"] | |
| if not api_key: | |
| st.error("OpenRouter API key is not set. Please add it to your secrets.") | |
| return | |
| # Convert image to base64 | |
| buffered = io.BytesIO() | |
| image.save(buffered, format="PNG") | |
| img_base64 = base64.b64encode(buffered.getvalue()).decode('utf-8') | |
| response = requests.post( | |
| url="https://openrouter.ai/api/v1/chat/completions", | |
| headers={ | |
| "Authorization": f"Bearer {api_key}", | |
| "Content-Type": "application/json" | |
| }, | |
| json={ | |
| "model": model_selection, | |
| "messages": [ | |
| { | |
| "role": "user", | |
| "content": [ | |
| {"type": "image_url", "image_url": {"url": f"data:image/png;base64,{img_base64}"}}, | |
| {"type": "text", "text": prompt_text} | |
| ] | |
| } | |
| ] | |
| } | |
| ) | |
| response.raise_for_status() # Will raise an HTTPError for bad responses (4xx or 5xx) | |
| result = response.json() | |
| ai_response = result['choices'][0]['message']['content'] | |
| st.markdown("---") | |
| st.subheader("Analysis Result:") | |
| st.markdown(ai_response) | |
| # --- ADD THIS BLOCK --- | |
| # Save results to session state for the report | |
| st.session_state['viz_ai_img_result'] = { | |
| "image": image, # The PIL Image object | |
| "prompt": prompt_text, | |
| "analysis": ai_response, | |
| "model": model_selection | |
| } | |
| st.success("✅ Analysis saved to the session report.") | |
| except requests.exceptions.HTTPError as http_err: | |
| st.error(f"HTTP error occurred: {http_err} - {response.text}") | |
| except Exception as e: | |
| st.error(f"An error occurred: {e}") | |
| elif analyze_button and not uploaded_image: | |
| st.warning("Please upload an image first.") |