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import gradio as gr | |
import google.generativeai as genai | |
import os | |
token=os.environ.get("TOKEN") | |
e = "" | |
genai.configure( | |
api_key=token | |
) | |
generation_config = { | |
"temperature": 1, | |
"top_p": 0.95, | |
"top_k": 64, | |
"max_output_tokens": 8192, | |
} | |
safety_settings = [ | |
{ | |
"category": "HARM_CATEGORY_HARASSMENT", | |
"threshold": "BLOCK_NONE" | |
}, | |
{ | |
"category": "HARM_CATEGORY_HATE_SPEECH", | |
"threshold": "BLOCK_NONE" | |
}, | |
{ | |
"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", | |
"threshold": "BLOCK_NONE" | |
}, | |
{ | |
"category": "HARM_CATEGORY_DANGEROUS_CONTENT", | |
"threshold": "BLOCK_NONE" | |
}, | |
] | |
mm = """ resous cet exercice. tu répondras en détaillant au maximum ton procédé de calcul. réponse attendue uniquement en Latex | |
""" | |
model = genai.GenerativeModel(model_name="gemini-1.5-pro", | |
generation_config=generation_config , | |
safety_settings=safety_settings) | |
# Fonction pour générer le contenu | |
def generate_content(image): | |
global e | |
if not image: | |
e =" djo" | |
else: | |
response = model.generate_content([mm,image]) | |
print(response.text) | |
e= response.text | |
return e | |
markdown = r""" | |
{e} | |
""".format(e=e) | |
iface = gr.Interface( | |
fn=generate_content, | |
inputs=gr.Image(type='pil'), | |
outputs=gr.Markdown( | |
latex_delimiters=[ | |
{"left": "$$", "right": "$$", "display": True}, | |
{"left": "$", "right": "$", "display": True}, | |
{"left": "\chemfig{", "right": "}", "display": True}, # Modifié | |
{"left": "\\(", "right": "\\)", "display": True}, | |
{"left": "\\begin{equation}", "right": "\\end{equation}", "display": True}, | |
{"left": "\\begin{align}", "right": "\\end{align}", "display": True}, | |
{"left": "\\begin{alignat}", "right": "\\end{alignat}", "display": True}, | |
{"left": "\\begin{gather}", "right": "\\end{gather}", "display": True}, | |
{"left": "\\begin{CD}", "right": "\\end{CD}", "display": True}, | |
{"left": "\\[", "right": "\\]", "display": True} | |
] | |
) | |
) | |
iface.launch() |