| import requests | |
| import gradio as gr | |
| import openai | |
| openai.api_key = "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" | |
| def infer(im): | |
| im.save("converted.png") | |
| url = "https://ajax.thehive.ai/api/demo/classify?endpoint=text_recognition" | |
| files = { | |
| "image": ("converted.png", open("converted.png", "rb"), "image/png"), | |
| "model_type": (None, "detection"), | |
| "media_type": (None, "photo"), | |
| } | |
| headers = {"referer": "https://thehive.ai/"} | |
| res = requests.post(url, headers=headers, files=files) | |
| text = "" | |
| blocks = [] | |
| for output in res.json()["response"]["output"]: | |
| text += output["block_text"] | |
| for poly in output["bounding_poly"]: | |
| blocks.append({ | |
| "text": "".join([c["class"] for c in poly["classes"]]), | |
| "rect": poly["dimensions"] | |
| }) | |
| def gpt(text): | |
| if not text: | |
| return "Veuillez saisir une question." | |
| f_prompt = f""" reformule ça | |
| {text}. """ | |
| response = openai.Completion.create( | |
| model="text-davinci-003", | |
| prompt=f_prompt, | |
| temperature=0.9, | |
| max_tokens=3500, | |
| top_p=1) | |
| answer = response.choices[0].text.strip() | |
| print(answer) | |
| return answer | |
| iface = gr.Interface( | |
| fn=infer, | |
| title="Mariam - Beta", | |
| description=" La maj du siècle. ", | |
| inputs=[gr.Image(type="pil")], | |
| outputs=gr.Text(), | |
| ).launch() |