improve format of the app

app.py

@@ -235,52 +235,71 @@ with demo:
             </div>
         """
     )
-    gr.Markdown(
-        """
+    with gr.Group():
+        with gr.Box():
+            with gr.Row(elem_id="prompt-container").style(
+                mobile_collapse=False, equal_height=True
+            ):
+                with gr.Column():
 
-    ## 1. Provide a vacancy and get back relevant resumes from an entire database of resumes for various roles.
-    """
-    )
-    text_vacancy = gr.Textbox(
-        hint="Paste here a Vacancy...", lines=7, label="Copy/paste here a vacancy"
-    )
-    b1 = gr.Button("Search Resume")
-    text_search_result = gr.Textbox(
-        hint="Top resumes will appear here ...",
-        label="Top resumes found in the database",
-    )
-    b1.click(search_resume, inputs=text_vacancy, outputs=text_search_result)
-    gr.Markdown(
-        """
+                    gr.Markdown(
+                        """
+                
+                    ## 1. Provide a vacancy and get back relevant resumes from an entire database of resumes for various roles.
+                    """
+                    )
+                    text_vacancy = gr.Textbox(
+                        hint="Paste here a Vacancy...",
+                        lines=7,
+                        label="Copy/paste here a vacancy",
+                    )
+                    b1 = gr.Button("Search Resume").style(
+                        margin=False,
+                        rounded=(False, True, True, False),
+                        full_width=False,
+                    )
+                    text_search_result = gr.Textbox(
+                        hint="Top resumes will appear here ...",
+                        label="Top resumes found in the database",
+                    )
+                    b1.click(
+                        search_resume, inputs=text_vacancy, outputs=text_search_result
+                    )
+                    gr.Markdown(
+                        """
+                
+                    ## 2. Select an appropriate resume for this vacancy, paste it in the textfield and get a relevant introduction email.
+                    """
+                    )
+                    text_resume = gr.Textbox(
+                        hint="Paste here a Resume...",
+                        label="Copy / Paste here your prefered resume from above and click the button to write an intro ",
+                    )
+                    b2 = gr.Button("Write a relevant intro").style(
+                        margin=False,
+                        rounded=(False, True, True, False),
+                        full_width=False,
+                    )
+                    gr.Markdown(
+                        """
+                
+                    ## 3. You have a relevant introduction email to send to the customer.
+                    """
+                    )
+                    text_intro = gr.Textbox(label="Intro Email")
+                    evaluation = gr.Textbox(label="Evaluation of the skills")
+                    b2.click(
+                        recruiting_assistant.create_intro,
+                        inputs=[text_vacancy, text_resume],
+                        outputs=[text_intro, evaluation],
+                    )
 
-    ## 2. Select an appropriate resume for this vacancy, paste it in the textfield and get a relevant introduction email.
-    """
-    )
-    text_resume = gr.Textbox(
-        hint="Paste here a Resume...",
-        label="Copy / Paste here your prefered resume from above and click the button to write an intro ",
-    )
-    b2 = gr.Button("Write a relevant intro")
-    gr.Markdown(
-        """
-
-    ## 3. You have a relevant introduction email to send to the customer.
-    """
-    )
-    text_intro = gr.Textbox(label="Intro Email")
-    evaluation = gr.Textbox(label="Evaluation of the skills")
-    b2.click(
-        recruiting_assistant.create_intro,
-        inputs=[text_vacancy, text_resume],
-        outputs=[text_intro, evaluation],
-    )
-
-    gr.Examples(
-        examples=examples,
-        fn=search_resume,
-        inputs=text_vacancy,
-        outputs=text_search_result,
-        cache_examples=False,
-    )
+                    gr.Examples(
+                        examples=examples,
+                        fn=search_resume,
+                        inputs=text_vacancy,
+                        outputs=text_search_result,
+                        cache_examples=False,
+                    )
 
 demo.launch()

intro.py

@@ -35,6 +35,7 @@ def call_openai(model, prompt):
     print("Got a language_response!")
     return result
 
+
 vacancy = """
 DATA SCIENTIST - GENTIS
 ========================

recruiting_assistant.py

@@ -81,8 +81,12 @@ def create_intro(vacancy=vacancy, resume=resume):
     ```
     """
 
-    prompt_vacancy_get_skills = ChatPromptTemplate.from_template(template=template_vacancy_get_skills)
-    vacancy_skills = LLMChain(llm=llm, prompt=prompt_vacancy_get_skills, output_key="vacancy_skills")
+    prompt_vacancy_get_skills = ChatPromptTemplate.from_template(
+        template=template_vacancy_get_skills
+    )
+    vacancy_skills = LLMChain(
+        llm=llm, prompt=prompt_vacancy_get_skills, output_key="vacancy_skills"
+    )
 
     template_resume_check_skills = """
     ```
@@ -101,8 +105,12 @@ def create_intro(vacancy=vacancy, resume=resume):
     ```
     """
 
-    prompt_resume_check_skills = ChatPromptTemplate.from_template(template=template_resume_check_skills)
-    resume_skills = LLMChain(llm=llm, prompt=prompt_resume_check_skills, output_key="resume_skills")
+    prompt_resume_check_skills = ChatPromptTemplate.from_template(
+        template=template_resume_check_skills
+    )
+    resume_skills = LLMChain(
+        llm=llm, prompt=prompt_resume_check_skills, output_key="resume_skills"
+    )
 
     template_resume_past_experiences = """
     Can you generate me a list of the past work experiences that the candidate has based on the resume below enclosed by three backticks.
@@ -113,8 +121,12 @@ def create_intro(vacancy=vacancy, resume=resume):
     ```
     """
 
-    prompt_resume_past_experiences = ChatPromptTemplate.from_template(template=template_resume_past_experiences)
-    past_experiences = LLMChain(llm=llm, prompt=prompt_resume_past_experiences, output_key="past_experiences")
+    prompt_resume_past_experiences = ChatPromptTemplate.from_template(
+        template=template_resume_past_experiences
+    )
+    past_experiences = LLMChain(
+        llm=llm, prompt=prompt_resume_past_experiences, output_key="past_experiences"
+    )
 
     template_vacancy_check_past_experiences = """
     ```
@@ -133,8 +145,14 @@ def create_intro(vacancy=vacancy, resume=resume):
     ```
     """
 
-    prompt_vacancy_check_past_experiences = ChatPromptTemplate.from_template(template=template_vacancy_check_past_experiences)
-    check_past_experiences = LLMChain(llm=llm, prompt=prompt_vacancy_check_past_experiences, output_key="check_past_experiences")
+    prompt_vacancy_check_past_experiences = ChatPromptTemplate.from_template(
+        template=template_vacancy_check_past_experiences
+    )
+    check_past_experiences = LLMChain(
+        llm=llm,
+        prompt=prompt_vacancy_check_past_experiences,
+        output_key="check_past_experiences",
+    )
 
     template_introduction_email = """
        You are a recruitment specialist that tries to place the right profiles for the right job.
@@ -159,31 +177,51 @@ def create_intro(vacancy=vacancy, resume=resume):
        Skills: print here a comma seperated list of the "skills_present" key of the JSON object {resume_skills}
        """
 
-    prompt_introduction_email = ChatPromptTemplate.from_template(template=template_introduction_email)
-    introduction_email = LLMChain(llm=llm, prompt=prompt_introduction_email, output_key="introduction_email")
+    prompt_introduction_email = ChatPromptTemplate.from_template(
+        template=template_introduction_email
+    )
+    introduction_email = LLMChain(
+        llm=llm, prompt=prompt_introduction_email, output_key="introduction_email"
+    )
 
     match_resume_vacancy_skills_chain = SequentialChain(
-        chains=[vacancy_skills, resume_skills, past_experiences, check_past_experiences, introduction_email],
+        chains=[
+            vacancy_skills,
+            resume_skills,
+            past_experiences,
+            check_past_experiences,
+            introduction_email,
+        ],
         input_variables=["vacancy", "resume"],
-        output_variables=["vacancy_skills", "resume_skills", "past_experiences", "check_past_experiences", "introduction_email"],
-        verbose=False
+        output_variables=[
+            "vacancy_skills",
+            "resume_skills",
+            "past_experiences",
+            "check_past_experiences",
+            "introduction_email",
+        ],
+        verbose=False,
     )
 
     result = match_resume_vacancy_skills_chain({"vacancy": vacancy, "resume": resume})
     print(result)
 
-    resume_skills = json.loads(result['resume_skills'])
+    resume_skills = json.loads(result["resume_skills"])
     relevant_skills = len(resume_skills["skills_present"])
-    total_skills = len(resume_skills["skills_present"] + resume_skills["skills_not_present"])
-    score_skills = round(100.0*(relevant_skills/total_skills),2)
-
+    total_skills = len(
+        resume_skills["skills_present"] + resume_skills["skills_not_present"]
+    )
+    score_skills = round(100.0 * (relevant_skills / total_skills), 2)
 
-    check_past_experiences = json.loads(result['check_past_experiences'])
+    check_past_experiences = json.loads(result["check_past_experiences"])
     relevant_experiences = len(check_past_experiences["relevant_experiences"])
-    total_experiences = len(check_past_experiences["relevant_experiences"] + check_past_experiences["irrelevant_experiences"])
-    score_experiences = round(100.0*(relevant_experiences/total_experiences),2)
+    total_experiences = len(
+        check_past_experiences["relevant_experiences"]
+        + check_past_experiences["irrelevant_experiences"]
+    )
+    score_experiences = round(100.0 * (relevant_experiences / total_experiences), 2)
 
-    new_line = '\n'
+    new_line = "\n"
 
     score = f"""
     Skills (Score: {score_skills}%)
@@ -192,5 +230,6 @@ def create_intro(vacancy=vacancy, resume=resume):
     """
     return result["introduction_email"], score
 
-if __name__ == '__main__':
-    create_intro(vacancy=vacancy,resume=resume)
+
+if __name__ == "__main__":
+    create_intro(vacancy=vacancy, resume=resume)

scripts/process-data.py

@@ -5,10 +5,10 @@
 import pandas as pd
 
 # Step 1: Read the parquet file
-df = pd.read_parquet('/Users/vincent/Downloads/csv-train.parquet')
+df = pd.read_parquet("/Users/vincent/Downloads/csv-train.parquet")
 
-if 'Category' in df.columns:
-    unique_classes = df['Category'].unique()
+if "Category" in df.columns:
+    unique_classes = df["Category"].unique()
     print("Unique classes in 'Category' column:")
     for cls in unique_classes:
         print(cls)
@@ -16,18 +16,18 @@ else:
     print("'Category' column does not exist in the data.")
 
 # Step 2: Check if 'Resume' column exists
-if 'Resume' in df.columns:
+if "Resume" in df.columns:
     # Keep only the 'Resume' column
     print(df.shape)
-    df = df.drop_duplicates(subset=['Resume'])
+    df = df.drop_duplicates(subset=["Resume"])
     print(df.shape)
-    df = df[['Resume']]
+    df = df[["Resume"]]
     # Remove all the new lines from each cell of the 'Resume' column
-    df['Resume'] = df['Resume'].replace('\n',' ', regex=True)
+    df["Resume"] = df["Resume"].replace("\n", " ", regex=True)
 else:
     print("'Resume' column does not exist in the data.")
 
 # Step 3: Write the filtered dataframe back to a csv file
-df.to_csv('/Users/vincent/Downloads/output.csv', index=False, header=False)
+df.to_csv("/Users/vincent/Downloads/output.csv", index=False, header=False)
 
 print("Completed successfully")