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| import os | |
| import requests | |
| import gradio as gr | |
| from PIL import Image | |
| import base64 | |
| # API Keys (set these in your environment or replace with your keys) | |
| nvidia_api_key = os.getenv("Vision") # NVIDIA API Key | |
| imagebb_api_key = os.getenv("Img") # Imgbb API Key | |
| # NVIDIA API Endpoint | |
| invoke_url = "https://ai.api.nvidia.com/v1/gr/meta/llama-3.2-90b-vision-instruct/chat/completions" | |
| def upload_image_to_imgbb(image_path): | |
| """Uploads an image to ImgBB and returns the URL.""" | |
| url = f"https://api.imgbb.com/1/upload?key={imagebb_api_key}" | |
| with open(image_path, "rb") as image_file: | |
| response = requests.post(url, files={"image": image_file}) | |
| if response.status_code == 200: | |
| return response.json()["data"]["url"] | |
| else: | |
| raise ValueError(f"Image upload failed: {response.json()}") | |
| def identify_skin_condition(image, instruction=("You are a dermatologist with over 20 years of experience trained to diagnose skin infections. " | |
| "Analyze the image and diagnose the skin. Write a sentence at the end to tell the person to consult or take their animals to their doctor if the skin infection is serious/chronic.")): | |
| """ | |
| Identifies the skin condition in the uploaded image using NVIDIA’s Llama 3.2 Vision Instruct model. | |
| This function yields progress messages as it executes. | |
| """ | |
| # Yield progress update | |
| yield "Saving image locally..." | |
| image_path = "uploaded_skin_image.jpeg" | |
| image.save(image_path) | |
| yield "Uploading image to ImgBB..." | |
| image_url = upload_image_to_imgbb(image_path) | |
| yield "Sending image to NVIDIA API..." | |
| headers = { | |
| "Authorization": f"Bearer {nvidia_api_key}", | |
| "Accept": "application/json" | |
| } | |
| payload = { | |
| "model": "meta/llama-3.2-90b-vision-instruct", | |
| "messages": [ | |
| { | |
| "role": "user", | |
| "content": [ | |
| {"type": "text", "text": instruction}, | |
| {"type": "image_url", "image_url": {"url": image_url}} | |
| ] | |
| } | |
| ], | |
| "max_tokens": 7200, | |
| "temperature": 0.15, | |
| "top_p": 0.25 | |
| } | |
| response = requests.post(invoke_url, headers=headers, json=payload) | |
| yield "Processing response..." | |
| response_data = response.json() | |
| if "choices" in response_data: | |
| result = response_data["choices"][0]["message"]["content"] | |
| else: | |
| result = f"Error in response: {response_data}" | |
| yield result | |
| # Gradio Interface (using live=True to stream progress updates) | |
| iface = gr.Interface( | |
| fn=identify_skin_condition, | |
| inputs=gr.Image(type="pil", label="Upload Skin Image"), | |
| outputs=gr.Markdown(label="Diagnosis and Recommendations"), | |
| title="SkinAid: Skin Infection Identifier App", | |
| description="Upload an image of your skin infection, and the app will identify the most likely condition and provide recommendations. Powered by AI", | |
| live=False, | |
| ) | |
| iface.launch() |