Update app.py

app.py

@@ -1,20 +1,17 @@
 import os
 import gradio as gr
-from langchain_google_genai.chat_models import ChatGoogleGenerativeAI
+from langchain_google_genai.chat_models import ChatGoogleGenerativeAI  # Import for Gemini
 from PIL import Image
 import torch
 from torchvision import models, transforms
 import json
 import requests
 
-# Load the secret value from Hugging Face Secrets Manager
-google_credentials = os.environ.get("GOOGLE_APPLICATION_CREDENTIALS")
+# Load credentials from Hugging Face's Secret Manager
+hf_token = os.environ.get("HF_TOKEN")  # Assuming the Hugging Face API token is set in environment
 
-# Parse the JSON string from the secret value
-credentials = json.loads(google_credentials)
-
-# Initialize the chat model with the necessary credentials
-llm = ChatGoogleGenerativeAI(model='gemini-1.5-pro')
+# Initialize Gemini model using Hugging Face
+llm = ChatGoogleGenerativeAI(model='gemini-1.5-pro')  # You can change 'gemini-1.5-pro' to the specific model you need
 
 # Load a pre-trained ResNet50 model for image classification
 model = models.resnet50(pretrained=True)
@@ -38,7 +35,7 @@ chat_history = []
 def chat_with_gemini(message):
     global chat_history
     # Get a response from the language model
-    bot_response = llm.predict(message)
+    bot_response = llm.predict(message)  # This will interact with the Gemini model
     chat_history.append((message, bot_response))
     return chat_history