app.py

import gradio as gr
from PIL import Image
import google.generativeai as genai
import time 

# Configure the API key directly in the script
API_KEY = 'AIzaSyDnnYRJ49VUm_2FiKhNubv85g6KCDjcNSc'
genai.configure(api_key=API_KEY)

# Generation configuration
generation_config = {
    "temperature": 1,
    "top_p": 0.95,
    "top_k": 64,
    "max_output_tokens": 8192,
    "response_mime_type": "text/plain",
}

# Safety settings
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"},
]

# Model name
MODEL_NAME = "gemini-1.5-pro-latest"


# Create the model
model = genai.GenerativeModel(
    model_name=MODEL_NAME,
    safety_settings=safety_settings,
    generation_config=generation_config,
)

e =""
# Fonction pour générer le contenu
async def generate_content(pro,image):
    global e

    if not image:
        response = model.generate_content(pro)
        print(response)
        e = response.text
        print(e)

    else:
        print(f"Uploading file...")
        uploaded_video = genai.upload_file(path=image)
        print(f"Completed upload: {uploaded_video.uri}")

        
        while uploaded_video.state.name == "PROCESSING":
            print("Waiting for video to be processed.")
            time.sleep(2)
            uploaded_video = genai.get_file(uploaded_video.name)

        if uploaded_video.state.name == "FAILED":
            raise ValueError(uploaded_video.state.name)
            
        print(f"Video processing complete: " + uploaded_video.uri)

        print("Making LLM inference request...")
        response = model.generate_content(
            [prompt, uploaded_video], request_options={"timeout": 600}
        )

        genai.delete_file(uploaded_video.name)
        print(f"Deleted file {uploaded_video.uri}")
        




        
        e = response
    return e


markdown = r"""
e
""".format(e)
# Interface Gradio
iface = gr.Interface(fn=generate_content, inputs=[gr.Textbox(),gr.Image(type='pil')], outputs= gr.Markdown(markdown, latex_delimiters=[{ "left":"$$", "right":"$$", "display": True }]))

iface.launch()