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| import gradio as gr | |
| from PIL import Image | |
| import os | |
| from together import Together | |
| import base64 | |
| import io | |
| # Initialize Together client | |
| client = None | |
| def initialize_client(api_key=None): | |
| global client | |
| api_key = os.getenv("TOGETHER_API_KEY") | |
| print(api_key) | |
| client = Together() | |
| def encode_image(image_path): | |
| with Image.open(image_path) as img: | |
| buffered = io.BytesIO() | |
| img.save(buffered, format="PNG") | |
| return base64.b64encode(buffered.getvalue()).decode("utf-8") | |
| def bot_streaming(image_path, history): | |
| max_new_tokens = 250 | |
| temperature = 0.7 | |
| if client is None: | |
| try: | |
| initialize_client() | |
| except Exception as e: | |
| history.append(("Error initializing client", f"{str(e)}")) | |
| yield history | |
| return | |
| prompt = """ | |
| You are expert in computer vision and autos inspection from image analysis. Inspect the auto's radiator core support for damage resulting from a front-end | |
| collision. Check for any bent, cracked, or misaligned components, including the core support bracket, mounting points, | |
| and frame sections. | |
| Ensure that the radiator is securely mounted and assess if there are any signs of stress or deformation caused by the accident. | |
| then tell it as accidental if not then not accidental. | |
| """ | |
| messages = [{"role": "system", "content": prompt}] | |
| # Encode the image and add to messages | |
| image_base64 = encode_image(image_path) | |
| messages.append({ | |
| "role": "user", | |
| "content": [ | |
| { | |
| "type": "image_url", | |
| "image_url": {"url": f"data:image/png;base64,{image_base64}"} | |
| } | |
| ] | |
| }) | |
| history = history + [("Image uploaded", "")] | |
| try: | |
| stream = client.chat.completions.create( | |
| model="meta-llama/Llama-Vision-Free", | |
| messages=messages, | |
| max_tokens=max_new_tokens, | |
| temperature=temperature, | |
| stream=True, | |
| ) | |
| response = "" | |
| for chunk in stream: | |
| if chunk.choices and chunk.choices[0].delta and chunk.choices[0].delta.content is not None: | |
| response += chunk.choices[0].delta.content | |
| history[-1] = ("Image uploaded", response) | |
| yield history | |
| if not response: | |
| history[-1] = ("Image uploaded", "No response generated. Please try again.") | |
| yield history | |
| except Exception as e: | |
| error_message = ( | |
| "The image is too large. Please try with a smaller image or compress the existing one." | |
| if "Request Entity Too Large" in str(e) | |
| else f"An error occurred: {str(e)}" | |
| ) | |
| history[-1] = ("Image uploaded", error_message) | |
| yield history | |
| # Set up Gradio interface | |
| with gr.Blocks() as demo: | |
| gr.Markdown("# Radiator Accident Detection") | |
| gr.Markdown("Upload an image of a radiator to determine if it shows signs of an accident") | |
| chatbot = gr.Chatbot() | |
| img = gr.Image(type="filepath", label="Upload Radiator Image") | |
| clear = gr.Button("Clear") | |
| img.upload(bot_streaming, inputs=[img, chatbot], outputs=chatbot) | |
| clear.click(lambda: None, None, chatbot, queue=False) | |
| if __name__ == "__main__": | |
| demo.launch(debug=True) |