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| import gradio as gr | |
| from openai import OpenAI | |
| import base64 | |
| import io | |
| import logging | |
| # Set up logging | |
| logging.basicConfig(level=logging.INFO) | |
| logger = logging.getLogger(__name__) | |
| def solve_stem_problem(api_key, image, subject="math"): | |
| # Initialize OpenAI client with user-provided API key | |
| try: | |
| client = OpenAI( | |
| base_url="https://openrouter.ai/api/v1", | |
| api_key=api_key, | |
| ) | |
| except Exception as e: | |
| logger.error(f"Failed to initialize OpenAI client: {str(e)}") | |
| return f"Error initializing API client: {str(e)}" | |
| # Define detective based on subject | |
| detectives = { | |
| "math": "Algebra Ace", | |
| "physics": "Physics Phantom", | |
| "chemistry": "Chemistry Clue-finder", | |
| "coding": "Code Cracker" | |
| } | |
| detective = detectives.get(subject, "Algebra Ace") | |
| # Encode the uploaded image to base64 | |
| try: | |
| # Convert the image to bytes | |
| img_byte_arr = io.BytesIO() | |
| image.save(img_byte_arr, format='PNG') | |
| img_byte_arr = img_byte_arr.getvalue() | |
| # Encode to base64 | |
| encoded_image = base64.b64encode(img_byte_arr).decode('utf-8') | |
| image_url_data = f"data:image/png;base64,{encoded_image}" | |
| except Exception as e: | |
| logger.error(f"Image encoding error: {str(e)}") | |
| return f"Error encoding image: {str(e)}" | |
| # Call the API with error handling | |
| try: | |
| completion = client.chat.completions.create( | |
| extra_headers={ | |
| "HTTP-Referer": "https://stem-sleuth.example.com", | |
| "X-Title": "STEM Sleuth", | |
| }, | |
| # Using a more stable model (adjust based on OpenRouter's available models) | |
| model="google/gemini-flash-1.5", | |
| messages=[ | |
| { | |
| "role": "user", | |
| "content": [ | |
| { | |
| "type": "text", | |
| "text": f"Act as {detective} and solve this {subject} problem step-by-step with a detective narrative." | |
| }, | |
| { | |
| "type": "image_url", | |
| "image_url": {"url": image_url_data} | |
| } | |
| ] | |
| } | |
| ] | |
| ) | |
| # Detailed response checking | |
| if not completion.choices: | |
| logger.warning("API returned no choices") | |
| return "API returned no choices. Please check model availability or API key permissions." | |
| if not completion.choices[0].message: | |
| logger.warning("API returned no message content") | |
| return "API returned no message content. Please try again or check the model." | |
| solution = completion.choices[0].message.content | |
| logger.info("Successfully retrieved solution") | |
| return solution | |
| except Exception as e: | |
| logger.error(f"API call failed: {str(e)}") | |
| return f"Error calling API: {str(e)}. Please verify model availability or try again later." | |
| # Create Gradio interface | |
| with gr.Blocks() as app: | |
| gr.Markdown("# STEM Sleuth Problem Solver") | |
| gr.Markdown("Upload an image of a STEM problem, select the subject, and provide your API key to get a step-by-step solution.") | |
| with gr.Row(): | |
| api_key_input = gr.Textbox(label="OpenRouter API Key", type="password", placeholder="Enter your API key") | |
| subject_input = gr.Dropdown( | |
| choices=["math", "physics", "chemistry", "coding"], | |
| label="Subject", | |
| value="math" | |
| ) | |
| image_input = gr.Image(type="pil", label="Upload Problem Image") | |
| solve_button = gr.Button("Solve Problem") | |
| output = gr.Textbox(label="Solution", lines=10) | |
| solve_button.click( | |
| fn=solve_stem_problem, | |
| inputs=[api_key_input, image_input, subject_input], | |
| outputs=output | |
| ) | |
| # Launch the app | |
| app.launch() |