Update app.py

app.py

@@ -1,15 +1,20 @@
 from ibm_watson_machine_learning.foundation_models import Model
 from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames as GenParams
 from ibm_watson_machine_learning.foundation_models.utils.enums import ModelTypes, DecodingMethods
+import gradio as gr
+
 
 # Set up the API key and project ID for IBM Watson
 watsonx_API = "Q7fLgZsmjZJ9L8BhLE_asx91M1C3dlPeYgmSm2wBleyk" # below is the instruction how to get them
 project_id= "a5a13c69-59c8-4856-bea6-4fa1f85e2cbb" # like "0blahblah-000-9999-blah-99bla0hblah0"
 
-generate_params = {
-    GenParams.MAX_NEW_TOKENS: 250
+# Generation parameters
+gen_parms = {
+    "max_new_tokens": 512,  # Adjust as needed for the length of the cover letter
+    "temperature": 0.7  # Adjust for creativity
 }
 
+
 model = Model(
     model_id = 'meta-llama/llama-2-13b-chat', # you can also specify like: ModelTypes.LLAMA_2_70B_CHAT
     params = generate_params,
@@ -20,7 +25,41 @@ model = Model(
     project_id= project_id
     )
 
-q = "How to be happy?"
-generated_response = model.generate(prompt=q)
-print(generated_response['results'][0]['generated_text'])
+# Initialize the model
+model = Model(model_id, credentials, gen_parms, project_id)
+
+# Function to generate customized career advice
+def generate_career_advice(field, position_name, current_qualifications, likes, skills):
+    # Craft the prompt for the model 
+    prompt = f"Generate a customized career advice using field: {field}, \
+    position_name: {position_name}, \
+    current_qualifications: {current_qualifications}, \
+    likes: {likes}, \
+    skills: {skills}."
+
+    generated_response = model.generate(prompt, gen_parms)
+
+    # Extract the generated text
+    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.
+    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'"),
+        gr.Textbox(label="Current Qualifications (e.g., studying in high school, high school diploma, college diploma, etc.)", placeholder="Enter your current qualifications ..."),
+        gr.Textbox(label="Likes (e.g., I like working with my hands, I like to work outside, I like to help people, I like teaching, ...", placeholder="Enter activities you like ...", lines=10),
+        gr.Textbox(label="Skills (e.g., I am good at math, science, languages, computers, research, hand tools, etc.)", placeholder="Skills ...", lines=10),
+    ],
+    outputs=gr.Textbox(label="Customized Career Advice"),
+    title="Customized Career Advice",
+    description="Generate a customized career advice using field, position name, likes and skills"
+)
+
+# Launch the application
+career_advice_app.launch(server_name="0.0.0.0", debug=True, server_port=7860, share=True)