willn9 commited on
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e8d7d34
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1 Parent(s): 597f9f1

Update app.py

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Files changed (1) hide show
  1. app.py +8 -8
app.py CHANGED
@@ -2,22 +2,23 @@
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  from ibm_watson_machine_learning.foundation_models import Model
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  import gradio as gr
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- watsonx_API = "Q7fLgZsmjZJ9L8BhLE_asx91M1C3dlPeYgmSm2wBleyk"
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- project_id= "a5a13c69-59c8-4856-bea6-4fa1f85e2cbb"
 
 
 
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  # Model and project settings
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  model_id = "meta-llama/llama-2-13b-chat" # Directly specifying the LLAMA2 model
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-
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  # Generation parameters
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  gen_parms = {
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  "max_new_tokens": 512, # Adjust as needed for the length of the cover letter
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  "temperature": 0.7 # Adjust for creativity
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  }
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-
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  # Initialize the model
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- model = Model(model_id, my_credentials, gen_parms, project_id, space_id, verify)
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  # Function to generate customized career advice
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  def generate_career_advice(field, position_name, current_qualifications, likes, skills):
@@ -34,11 +35,10 @@ def generate_career_advice(field, position_name, current_qualifications, likes,
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  career_advice = generated_response["results"][0]["generated_text"]
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  return career_advice
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-
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  # Create Gradio interface for the cover letter generation application
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  career_advice_app = gr.Interface(
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  fn=generate_career_advice,
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- allow_flagging="never", # Deactivate the flag function in gradio as it is not needed.
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  inputs=[
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  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'."),
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  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'"),
@@ -48,7 +48,7 @@ career_advice_app = gr.Interface(
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  ],
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  outputs=gr.Textbox(label="Customized Career Advice"),
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  title="Customized Career Advice",
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- description="Generate a customized career advice using field, position name, likes and skills"
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  )
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  # Launch the application
 
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  from ibm_watson_machine_learning.foundation_models import Model
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  import gradio as gr
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+ import os
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+
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+ # Securely load the API key and project ID
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+ watsonx_API = os.getenv("WATSONX_API")
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+ project_id= os.getenv("PROJECT_ID")
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  # Model and project settings
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  model_id = "meta-llama/llama-2-13b-chat" # Directly specifying the LLAMA2 model
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  # Generation parameters
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  gen_parms = {
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  "max_new_tokens": 512, # Adjust as needed for the length of the cover letter
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  "temperature": 0.7 # Adjust for creativity
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  }
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  # Initialize the model
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+ model = Model(model_id, watsonx_API, gen_parms, project_id)
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  # Function to generate customized career advice
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  def generate_career_advice(field, position_name, current_qualifications, likes, skills):
 
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  career_advice = generated_response["results"][0]["generated_text"]
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  return career_advice
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  # Create Gradio interface for the cover letter generation application
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  career_advice_app = gr.Interface(
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  fn=generate_career_advice,
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+ allow_flagging="never", # Deactivate the flag function in gradio as it is not needed.
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  inputs=[
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  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'."),
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  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'"),
 
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  ],
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  outputs=gr.Textbox(label="Customized Career Advice"),
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  title="Customized Career Advice",
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+ description="Generate a customized career advice using field, position name, likes, and skills"
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  )
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  # Launch the application