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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 generate a customized cover letter
+def generate_cover_letter(company_name, position_name, job_description, resume_content):
+    # Craft the prompt for the model to generate a cover letter
+    prompt = f"Generate a customized cover letter using the company name: {company_name}, the position applied for: {position_name}, and the job description: {job_description}. Ensure the cover letter highlights my qualifications and experience as detailed in the resume content: {resume_content}. Adapt the content carefully to avoid including experiences not present in my resume but mentioned in the job description. The goal is to emphasize the alignment between my existing skills and the requirements of the role."
+
+    messages = [
+    {
+        "role": "user",
+        "content": [
+            {
+                "type": "text",
+                "text": prompt
+            },
+        ]
+    }
+]
+
+    # Generate a response using the model with parameters
+    generated_response = model.chat(messages=messages)
+
+    # Extract and return the generated text
+    cover_letter = generated_response['choices'][0]['message']['content']
+
+    return cover_letter
+
+# Create Gradio interface for the cover letter generation application
+cover_letter_app = gr.Interface(
+    fn=generate_cover_letter,
+    flagging_mode="never", # Deactivate the flag function in gradio as it is not needed.
+    inputs=[
+        gr.Textbox(label="Company Name", placeholder="Enter the name of the company..."),
+        gr.Textbox(label="Position Name", placeholder="Enter the name of the position..."),
+        gr.Textbox(label="Job Description Information", placeholder="Paste the job description here...", lines=10),
+        gr.Textbox(label="Resume Content", placeholder="Paste your resume content here...", lines=10),
+    ],
+    outputs=gr.Textbox(label="Customized Cover Letter"),
+    title="Customized Cover Letter Generator",
+    description="Generate a customized cover letter by entering the company name, position name, job description and your resume."
+)
+
+# Launch the application
+cover_letter_app.launch()

requirements.txt

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+Gradio
+ibm_watsonx_ai