Update app.py

app.py

@@ -1,39 +1,39 @@
 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)
 
-import os 
-token=os.environ.get("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
+# 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"},
+]
 
-# Chargez l'image
+# Model name
+MODEL_NAME = "gemini-1.5-pro-latest"
 
-gemini_pro = GeminiMultiModal(model_name="models/gemini-pro-vision")
 
-llm = Gemini(model="models/gemini-1.5-pro")
+# Create the model
+model = genai.GenerativeModel(
+    model_name=MODEL_NAME,
+    safety_settings=safety_settings,
+    generation_config=generation_config,
+)
 
 e =""
 # Fonction pour générer le contenu
@@ -41,16 +41,41 @@ async def generate_content(pro,image):
     global e
 
     if not image:
-        response = await llm.acomplete(pro,safety_settings=safe)
+        response = model.generate_content(pro)
         print(response)
         e = response.text
         print(e)
 
     else:
-        #response = model.generate_content([pro, image])
-        response_acomplete = await llm.acomplete(prompt=pro, image_documents=image,safety_settings=safe)
-        print(response_acomplete)
-        e = response_acomplete
+        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