Update app.py

app.py

@@ -1,28 +1,56 @@
 import gradio as gr
-import google.generativeai as genai
+
 
 import os 
 token=os.environ.get("TOKEN") 
-genai.configure(api_key=token)
+os.environ["GOOGLE_API_KEY"] = token
+
+safe = [
+    {
+        "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",
+    },
+]
+from llama_index.llms.gemini import Gemini
+from llama_index.multi_modal_llms.gemini import GeminiMultiModal
+
+from llama_index.core.multi_modal_llms.generic_utils import load_image_urls
+
 
 # Chargez l'image
-model = genai.GenerativeModel(model_name="gemini-pro-vision")
-model_simple = genai.GenerativeModel(model_name="gemini-pro")
+
+gemini_pro = GeminiMultiModal(model_name="models/gemini-pro-vision")
+
+llm = Gemini(model="models/gemini-pro")
 
 e =""
 # Fonction pour générer le contenu
-def generate_content(pro,image):
+async def generate_content(pro,image):
     global e
 
     if not image:
-        response = model_simple.generate_content(pro)
+        response = await llm.acomplete(pro,safety_settings=safe)
+        print(response)
         e = response.text
         print(e)
 
     else:
-        response = model.generate_content([pro, image])
-        print(response.text)
-        e = response.text
+        #response = model.generate_content([pro, image])
+        response_acomplete = await gemini_pro.acomplete(prompt=pro, image_documents=image,)
+        print(response_acomplete)
+        e = response_acomplete
     return e