Update app.py

app.py

@@ -1,76 +1,75 @@
-# Import necessary packages
-from ibm_watsonx_ai import Credentials
-from ibm_watsonx_ai import APIClient
-from ibm_watsonx_ai.foundation_models import Model, ModelInference
-from ibm_watsonx_ai.foundation_models.schema import TextChatParameters
-from ibm_watsonx_ai.metanames import GenTextParamsMetaNames
-import gradio as gr
-
-# Model and project settings
-model_id = "meta-llama/llama-3-2-11b-vision-instruct"  # Directly specifying the LLAMA3 model
-
-watsonx_API="L0sx3BXcQRWNmz45mbBLxL1UiZGnftHFQTwITAci-523"
-project_id="ed8f7a2c-e597-4a09-a98f-dbdcef57a0d0"
-
-# Set credentials to use the model
-credentials = {
-                   "url" : "https://au-syd.ml.cloud.ibm.com",
-                   "apikey": watsonx_API
-}
-
-# Generation parameters
-params = TextChatParameters(
-    temperature=0.7,
-    max_tokens=1024
-)
-
-project_id = "skills-network"
-
-# Initialize the model
-model = ModelInference(
-    model_id=model_id,
-    credentials=credentials,
-    project_id=project_id,
-    params=params
-)
-
-# Function to generate career advice
-def generate_career_advice(position_applied, job_description, resume_content):
-    # The prompt for the model
-    prompt = f"Considering the job description: {job_description}, and the resume provided: {resume_content}, identify areas for enhancement in the resume. Offer specific suggestions on how to improve these aspects to better match the job requirements and increase the likelihood of being selected for the position of {position_applied}."
-
-    messages = [
-    {
-        "role": "user",
-        "content": [
-            {
-                "type": "text",
-                "text": prompt
-            },
-        ]
-    }
-]
-
-    # Generate a response using the model with parameters
-    generated_response = model.chat(messages=messages)
-
-    # Extract and format the generated text
-    advice = generated_response['choices'][0]['message']['content']
-    return advice
-
-# Create Gradio interface for the career advice application
-career_advice_app = gr.Interface(
-    fn=generate_career_advice,
-    flagging_mode="never", # Deactivate the flag function in gradio as it is not needed.
-    inputs=[
-        gr.Textbox(label="Position Applied For", placeholder="Enter the position you are applying for..."),
-        gr.Textbox(label="Job Description Information", placeholder="Paste the job description here...", lines=10),
-        gr.Textbox(label="Your Resume Content", placeholder="Paste your resume content here...", lines=10),
-    ],
-    outputs=gr.Textbox(label="Advice"),
-    title="Career Advisor",
-    description="Enter the position you're applying for, paste the job description, and your resume content to get advice on what to improve for getting this job."
-)
-
-# Launch the application
+# Import necessary packages
+from ibm_watsonx_ai import Credentials
+from ibm_watsonx_ai import APIClient
+from ibm_watsonx_ai.foundation_models import Model, ModelInference
+from ibm_watsonx_ai.foundation_models.schema import TextChatParameters
+from ibm_watsonx_ai.metanames import GenTextParamsMetaNames
+import gradio as gr
+
+# Model and project settings
+model_id = "meta-llama/llama-3-2-11b-vision-instruct"  # Directly specifying the LLAMA3 model
+
+watsonx_API="L0sx3BXcQRWNmz45mbBLxL1UiZGnftHFQTwITAci-523"
+project_id="ed8f7a2c-e597-4a09-a98f-dbdcef57a0d0"
+
+# Set credentials to use the model
+credentials = {
+                   "url" : "https://au-syd.ml.cloud.ibm.com",
+                   "apikey": watsonx_API
+}
+
+# Generation parameters
+params = TextChatParameters(
+    temperature=0.7,
+    max_tokens=1024
+)
+
+
+# Initialize the model
+model = ModelInference(
+    model_id=model_id,
+    credentials=credentials,
+    project_id=project_id,
+    params=params
+)
+
+# Function to generate career advice
+def generate_career_advice(position_applied, job_description, resume_content):
+    # The prompt for the model
+    prompt = f"Considering the job description: {job_description}, and the resume provided: {resume_content}, identify areas for enhancement in the resume. Offer specific suggestions on how to improve these aspects to better match the job requirements and increase the likelihood of being selected for the position of {position_applied}."
+
+    messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "text",
+                "text": prompt
+            },
+        ]
+    }
+]
+
+    # Generate a response using the model with parameters
+    generated_response = model.chat(messages=messages)
+
+    # Extract and format the generated text
+    advice = generated_response['choices'][0]['message']['content']
+    return advice
+
+# Create Gradio interface for the career advice application
+career_advice_app = gr.Interface(
+    fn=generate_career_advice,
+    flagging_mode="never", # Deactivate the flag function in gradio as it is not needed.
+    inputs=[
+        gr.Textbox(label="Position Applied For", placeholder="Enter the position you are applying for..."),
+        gr.Textbox(label="Job Description Information", placeholder="Paste the job description here...", lines=10),
+        gr.Textbox(label="Your Resume Content", placeholder="Paste your resume content here...", lines=10),
+    ],
+    outputs=gr.Textbox(label="Advice"),
+    title="Career Advisor",
+    description="Enter the position you're applying for, paste the job description, and your resume content to get advice on what to improve for getting this job."
+)
+
+# Launch the application
 career_advice_app.launch()