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| import gradio as gr | |
| from huggingface_hub import InferenceClient | |
| """ | |
| For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference | |
| """ | |
| # client = InferenceClient("HuggingFaceH4/zephyr-7b-beta") | |
| from google.cloud import storage | |
| from google.oauth2 import service_account | |
| import json | |
| import os | |
| import requests | |
| # upload image to google cloud storage | |
| def upload_file_to_gcs_blob(file): | |
| google_creds = os.environ.get("GOOGLE_APPLICATION_CREDENTIALS_JSON") | |
| creds_json = json.loads(google_creds) | |
| credentials = service_account.Credentials.from_service_account_info(creds_json) | |
| # Google Cloud credentials | |
| storage_client = storage.Client(credentials=credentials, project=creds_json['project_id']) | |
| bucket_name=os.environ.get('bucket_name') | |
| bucket = storage_client.bucket(bucket_name) | |
| destination_blob_name = os.path.basename(file) | |
| blob = bucket.blob(destination_blob_name) | |
| blob.upload_from_filename(file) | |
| public_url = blob.public_url | |
| return public_url | |
| from PIL import Image | |
| def is_image(file_path): | |
| try: | |
| Image.open(file_path) | |
| return True | |
| except IOError: | |
| return False | |
| # def respond( | |
| # message, | |
| # history: list[tuple[str, str]], | |
| # system_message, | |
| # max_tokens, | |
| # temperature, | |
| # top_p, | |
| # ): | |
| # messages = [{"role": "system", "content": system_message}] | |
| # for val in history: | |
| # if val[0]: | |
| # messages.append({"role": "user", "content": val[0]}) | |
| # if val[1]: | |
| # messages.append({"role": "assistant", "content": val[1]}) | |
| # messages.append({"role": "user", "content": message}) | |
| # response = "" | |
| # for message in client.chat_completion( | |
| # messages, | |
| # max_tokens=max_tokens, | |
| # stream=True, | |
| # temperature=temperature, | |
| # top_p=top_p, | |
| # ): | |
| # token = message.choices[0].delta.content | |
| # response += token | |
| # yield response | |
| def get_completion(message,history): | |
| if message["text"].strip() == "" and not message["files"]: | |
| raise gr.Error("Please input a query and optionally image(s).") | |
| if message["text"].strip() == "" and message["files"]: | |
| raise gr.Error("Please input a text query along the image(s).") | |
| text = message['text'] | |
| content = [ | |
| {"type": "text", "text": text}, | |
| ] | |
| if message['files']: | |
| file = message['files'][0] | |
| public_url = upload_file_to_gcs_blob(file) | |
| if is_image(file): # only support image file now | |
| content_image = { | |
| "type": "image_url", | |
| "image_url": { | |
| "url": public_url, | |
| },} | |
| content.append(content_image) | |
| else: | |
| raise gr.Error("Only support image files now.") | |
| history_openai_format = [] | |
| for human, assistant in history: | |
| # check if there is image info in the history message | |
| if isinstance(human, tuple): | |
| continue | |
| history_openai_format.append({"role": "user", "content": human }) | |
| history_openai_format.append({"role": "assistant", "content":assistant}) | |
| history_openai_format.append({"role": "user", "content": content}) | |
| print(history_openai_format) | |
| system_message = '''You are GPT-4o("o" for omni), OpenAI's new flagship model that can reason across audio, vision, and text in real time. | |
| GPT-4o matches GPT-4 Turbo performance on text in English and code, with significant improvement on text in non-English languages, while also being much faster. | |
| GPT-4o is especially better at vision and audio understanding compared to existing models. | |
| GPT-4o's text and image capabilities are avaliable for users now. More capabilities like audio, and video will be rolled out iteratively in the future. | |
| ''' | |
| # headers | |
| openai_api_key = os.environ.get('openai_api_key') | |
| headers = { | |
| 'Authorization': f'Bearer {openai_api_key}' | |
| } | |
| temperature = 0.7 | |
| max_tokens = 2048 | |
| init_message = [{"role": "system", "content": system_message}] | |
| messages = init_message + history_openai_format[-5:], #system message + latest 2 round dialogues + user input | |
| # request body | |
| data = { | |
| 'model': 'gpt-4o', # we use gpt-4o here | |
| 'messages': messages, | |
| 'temperature':temperature, | |
| 'max_tokens':max_tokens, | |
| # 'stream':True， | |
| } | |
| # get response | |
| response = requests.post('https://burn.hair/v1/chat/completions', headers=headers, json=data) | |
| response_data = response.json() | |
| if 'error' in response_data: | |
| response_content = response_data['error']['message'] | |
| else: | |
| response_content = response_data['choices'][0]['message']['content'] | |
| usage = response_data['usage'] | |
| return response_content | |
| """ | |
| For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface | |
| """ | |
| demo = gr.ChatInterface( | |
| get_completion, | |
| multimodal=True, | |
| # additional_inputs=[ | |
| # gr.Textbox(value="You are a friendly Chatbot.", label="System message"), | |
| # gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"), | |
| # gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"), | |
| # ], | |
| ) | |
| demo.queue(max_size = 10) | |
| if __name__ == "__main__": | |
| demo.launch() |