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app.py

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+# 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=512
+)
+
+# Initialize the model
+model = ModelInference(
+    model_id=model_id,
+    credentials=credentials,
+    project_id=project_id,
+    params=params
+)
+
+# Function to polish the resume using the model, making polish_prompt optional
+def polish_resume(position_name, resume_content, polish_prompt=""):
+    # Check if polish_prompt is provided and adjust the combined_prompt accordingly
+    if polish_prompt and polish_prompt.strip():
+        prompt_use = f"Given the resume content: '{resume_content}', polish it based on the following instructions: {polish_prompt} for the {position_name} position."
+    else:
+        prompt_use = f"Suggest improvements for the following resume content: '{resume_content}' to better align with the requirements and expectations of a {position_name} position. Return the polished version, highlighting necessary adjustments for clarity, relevance, and impact in relation to the targeted role."
+
+    messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "text",
+                "text": prompt_use
+            },
+        ]
+    }
+]
+    # Generate a response using the model with parameters
+    generated_response = model.chat(messages=messages)
+
+    # Extract and return the generated text
+    generated_text = generated_response['choices'][0]['message']['content']
+
+    return generated_text
+
+# Create Gradio interface for the resume polish application, marking polish_prompt as optional
+resume_polish_application = gr.Interface(
+    fn=polish_resume,
+    flagging_mode="never", # Deactivate the flag function in gradio as it is not needed.
+    inputs=[
+        gr.Textbox(label="Position Name", placeholder="Enter the name of the position..."),
+        gr.Textbox(label="Resume Content", placeholder="Paste your resume content here...", lines=20),
+        gr.Textbox(label="Polish Instruction (Optional)", placeholder="Enter specific instructions or areas for improvement (optional)...", lines=2),
+    ],
+    outputs=gr.Textbox(label="Polished Content"),
+    title="Resume Polish Application",
+    description="This application helps you polish your resume. Enter the position your want to apply, your resume content, and specific instructions or areas for improvement (optional), then get a polished version of your content."
+)
+
+# Launch the application
+resume_polish_application.launch(share=True)

requirements.txt

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+Gradio
+ibm_watsonx_ai