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| import gradio as gr | |
| import google.generativeai as genai | |
| import os | |
| # Set up Gemini API key (use environment variable on Hugging Face) | |
| API_KEY = os.getenv("GEMINI_API_KEY") # Set this in Hugging Face secrets | |
| genai.configure(api_key=API_KEY) | |
| # Use Gemini 2.0 Flash (free-tier accessible) | |
| model = genai.GenerativeModel("gemini-2.0-flash") | |
| def vqa(image, question): | |
| """Performs Visual Question Answering (VQA) using the Gemini API.""" | |
| try: | |
| response = model.generate_content([question, image]) | |
| return response.text | |
| except Exception as e: | |
| return f"Error: {e}" | |
| # Create Gradio interfaces | |
| vqa_interface = gr.Interface( | |
| fn=vqa, | |
| inputs=[gr.Image(type="filepath"), gr.Textbox(label="Question")], | |
| outputs=gr.Textbox(label="Answer"), | |
| title="Visual Question Answering (VQA)", | |
| description="Upload an image and ask a question about it." | |
| ) | |
| if __name__ == "__main__": | |
| vqa_interface.launch() | |