Update app.py

app.py

@@ -2,14 +2,10 @@
 from ibm_watson_machine_learning.foundation_models import Model
 import gradio as gr
 
-import os
-
-# Securely load the API key and project ID
-watsonx_API = os.getenv("watsonx_API")
-project_id= os.getenv("project_id")
 
 # Model and project settings
 model_id = "meta-llama/llama-2-13b-chat"  # Directly specifying the LLAMA2 model
+
 # Set credentials to use the model
 my_credentials = {
     "url": "https://us-south.ml.cloud.ibm.com"
@@ -27,7 +23,6 @@ verify = False
 # Initialize the model
 model = Model(model_id, my_credentials, gen_parms, project_id, space_id, verify)
 
-
 # Function to generate customized career advice
 def generate_career_advice(field, position_name, current_qualifications, likes, skills):
     # Craft the prompt for the model 
@@ -43,10 +38,11 @@ def generate_career_advice(field, position_name, current_qualifications, likes,
     career_advice = generated_response["results"][0]["generated_text"]
     return career_advice
 
+
 # Create Gradio interface for the cover letter generation application
 career_advice_app = gr.Interface(
     fn=generate_career_advice,
-    allow_flagging="never",  # Deactivate the flag function in gradio as it is not needed.
+    allow_flagging="never", # Deactivate the flag function in gradio as it is not needed.
     inputs=[
         gr.Textbox(label="Field of Interest (e.g., healthcare, trades, social service, etc., or enter 'not sure')", placeholder="Enter the field which you are interested in... or type 'not sure'."),
         gr.Textbox(label="Position Name (e.g., nurse, personal support worker, software developer, plumber, etc., or enter 'not sure')", placeholder="Enter the name of the position you are interested in... or type 'not sure'"),
@@ -56,8 +52,8 @@ career_advice_app = gr.Interface(
     ],
     outputs=gr.Textbox(label="Customized Career Advice"),
     title="Customized Career Advice",
-    description="Generate a customized career advice using field, position name, likes, and skills"
+    description="Generate a customized career advice using field, position name, likes and skills"
 )
 
 # Launch the application
-career_advice_app.launch()
+career_advice_app.launch(server_name="0.0.0.0", debug=True, server_port=7860, share=True)