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| import streamlit as st | |
| import requests | |
| import base64 | |
| # Function to encode image in base64 | |
| def encode_image(image): | |
| return base64.b64encode(image.read()).decode('utf-8') | |
| # Function to send data to the GPT-4 Vision API | |
| def analyze_images(images, use_case, api_key): | |
| headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"} | |
| prompt_text = ( | |
| f"This is a prototype for {use_case}.\n" | |
| "Review each screenshot carefully, focusing on different aspects of usability.\n" | |
| "Evaluate each screenshot based on Nielsen's 10 usability heuristics:\n" | |
| "- Visibility of system status\n" | |
| "- Match between system and the real world\n" | |
| "- User control and freedom\n" | |
| "- Consistency and standards\n" | |
| "- Error prevention\n" | |
| "- Recognition rather than recall\n" | |
| "- Flexibility and efficiency of use\n" | |
| "- Aesthetic and minimalist design\n" | |
| "- Help users recognize, diagnose, and recover from errors\n" | |
| "- Help and documentation\n" | |
| "Create a table with the following columns:\n" | |
| "Heuristic evaluated, Comments (Provide specific observations and suggestions for improvement)" | |
| ) | |
| messages_content = [ | |
| { | |
| "role": "user", | |
| "content": [ | |
| {"type": "text", "text": prompt_text} | |
| ] | |
| } | |
| ] | |
| for image in images: | |
| base64_image = encode_image(image) | |
| messages_content[0]["content"].append({ | |
| "type": "image_url", | |
| "image_url": {"url": f"data:image/jpeg;base64,{base64_image}"} | |
| }) | |
| data = { | |
| "model": "gpt-4-turbo", | |
| "messages": messages_content, | |
| "max_tokens": 1024 # Adjusted max_tokens to give more room for detailed analysis | |
| } | |
| response = requests.post("https://api.openai.com/v1/chat/completions", headers=headers, json=data) | |
| return response.json() | |
| # Streamlit UI setup | |
| st.title("Auto Heuristic Evaluation") | |
| uploaded_files = st.file_uploader("Upload images (up to 5)", accept_multiple_files=True, type=['jpg', 'jpeg', 'png']) | |
| use_case = st.text_input("Use Case - Describe the use case for your prototype") | |
| api_key = st.text_input("OpenAI API Key", type="password") | |
| if st.button("Analyze Images"): | |
| if not uploaded_files: | |
| st.warning("Please upload at least one image.") | |
| elif not use_case: | |
| st.warning("Please enter the use case description.") | |
| elif not api_key: | |
| st.warning("Please enter the OpenAI API key.") | |
| else: | |
| with st.spinner("Analyzing images..."): | |
| analysis_result = analyze_images(uploaded_files, use_case, api_key) | |
| if 'error' in analysis_result: | |
| st.error(f"Analysis failed: {analysis_result.get('error', {}).get('message', 'Unknown error')}") | |
| else: | |
| st.success("Analysis complete!") | |
| if 'choices' in analysis_result and analysis_result['choices']: | |
| content = analysis_result['choices'][0]['message']['content'] | |
| st.markdown(content) | |
| else: | |
| st.warning("No detailed results available.") | |