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| import gradio as gr | |
| import os | |
| import requests | |
| from dotenv import load_dotenv | |
| load_dotenv() | |
| API_TOKEN = os.environ.get("API_TOKEN", None) | |
| MODEL_URL = os.environ.get("MODEL_URL", None) | |
| def evaluate(hotel_request: str): | |
| resp = requests.post( | |
| MODEL_URL, | |
| json={"inputs": hotel_request}, | |
| headers={"Authorization": f"Bearer {API_TOKEN}"}, | |
| cookies=None, | |
| timeout=10, | |
| ) | |
| payload = resp.json() | |
| text = payload[0]["generated_text"] | |
| name, location, hotel, date = text.split("|") | |
| return name, hotel, location, date | |
| gr.Interface( | |
| fn=evaluate, | |
| inputs=[ | |
| # gr.components.Textbox( | |
| # lines=2, | |
| # label="Instruction", | |
| # placeholder="Tell me about alpacas.", | |
| # ), | |
| gr.components.Textbox(lines=2, label="Input", placeholder="Request for the Hotel"), | |
| # gr.components.Slider( | |
| # minimum=0, maximum=1, value=0.1, label="Temperature" | |
| # ), | |
| # gr.components.Slider( | |
| # minimum=0, maximum=1, value=0.75, label="Top p" | |
| # ), | |
| # gr.components.Slider( | |
| # minimum=0, maximum=100, step=1, value=40, label="Top k" | |
| # ), | |
| # gr.components.Slider( | |
| # minimum=1, maximum=4, step=1, value=4, label="Beams" | |
| # ), | |
| # gr.components.Slider( | |
| # minimum=1, maximum=2000, step=1, value=128, label="Max tokens" | |
| # ), | |
| # gr.components.Checkbox(label="Stream output"), | |
| ], | |
| outputs=[ | |
| gr.inputs.Textbox( | |
| lines=1, | |
| label="Guest Name", | |
| ), | |
| gr.inputs.Textbox( | |
| lines=1, | |
| label="Hotel", | |
| ), | |
| gr.inputs.Textbox( | |
| lines=1, | |
| label="Location", | |
| ), | |
| gr.inputs.Textbox( | |
| lines=1, | |
| label="Date", | |
| ) | |
| ], | |
| allow_flagging="never", | |
| title="Falcon-LoRA", | |
| description="Falcon-LoRA is a 1B-parameter LLM finetuned to follow instructions. It is trained on the [Hotel Requests](https://huggingface.co/datasets/MichaelAI23/hotel_requests) dataset.", # noqa: E501 | |
| ).queue().launch() #server_name="0.0.0.0", server_port=8080) |