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main.py

@@ -3,9 +3,10 @@ import os
 import keras_nlp
 from transformers import AutoModelForCausalLM
 
-# Set Kaggle API credentials
-os.environ["KAGGLE_USERNAME"] = "rogerkorantenng"
-os.environ["KAGGLE_KEY"] = "9a33b6e88bcb6058b1281d777fa6808d"
+# Set Kaggle API credentials using values from environment variables
+os.environ["KAGGLE_USERNAME"] = os.environ.get("KAGGLE_USERNAME")
+os.environ["KAGGLE_KEY"] = os.environ.get("KAGGLE_KEY")
+
 
 # Load LoRA weights if you have them
 LoRA_weights_path = "fined-tuned-model.lora.h5"
@@ -38,10 +39,10 @@ interface = gr.Interface(
     fn=generate_response,  # Function that generates responses
     inputs=gr.Textbox(placeholder="Hello, I am Sage, your mental health advisor", lines=2, scale=7),
     outputs=gr.Textbox(),
-    title="Welcome to Sage, your dedicated mental health advisor.",
+    title="Sage, your Mental Health Advisor",
 #     description="Chat with Sage, your mental health advisor.",
 #     live=True
 )
-
+proxy_prefix = os.environ.get("PROXY_PREFIX")
 # Launch the Gradio app
-interface.launch(share=True, share_server_address="hopegivers.tech:7000")
+interface.launch(server_name="0.0.0.0", server_port=8080, root_path=proxy_prefix, share=True)