Added character to the write up and moved functions to the helpers file.

app.py

@@ -20,6 +20,8 @@ import json
 import time
 import os
 
+import resume_helpers
+
 
 # Set the LANGCHAIN_TRACING_V2 environment variable to 'true'
 os.environ['LANGCHAIN_TRACING_V2'] = 'true'
@@ -29,102 +31,25 @@ os.environ['LANGCHAIN_PROJECT'] = 'Resume_Project'
 
 load_dotenv()
 
-def pdf_to_string(file):
-    """
-    Convert a PDF file to a string.
-
-    Parameters:
-    file (io.BytesIO): A file-like object representing the PDF file.
-
-    Returns:
-    str: The extracted text from the PDF.
-    """
-    pdf_reader = PyPDF2.PdfReader(file)
-    num_pages = len(pdf_reader.pages)
-    text = ''
-    for i in range(num_pages):
-        page = pdf_reader.pages[i]
-        text += page.extract_text()
-    file.close()
-    return text
-
-class CustomOutputParserException(Exception):
-    pass
-
-def extract_resume_fields(full_text, model):
-    """
-    Analyze a resume text and extract structured information using a specified language model.
-    Parameters:
-    full_text (str): The text content of the resume.
-    model (str): The language model object to use for processing the text.
-    Returns:
-    dict: A dictionary containing structured information extracted from the resume.
-    """
-    # The Resume object is imported from the local resume_template file
-
-    with open("prompts/resume_extraction.prompt", "r") as f:
-        template = f.read()
-
-    parser = PydanticOutputParser(pydantic_object=Resume)
-
-    prompt_template = PromptTemplate(
-        template=template,
-        input_variables=["resume"],
-        partial_variables={"response_template": parser.get_format_instructions()},
-    )
-    llm = llm_dict.get(model, ChatOpenAI(temperature=0, model=model))
-
-    chain = prompt_template | llm | parser
-    max_attempts = 3
-    attempt = 1
-
-    while attempt <= max_attempts:
-        try:
-            output = chain.invoke(full_text)
-            print(output)
-            return output
-        except (CustomOutputParserException, ValidationError) as e:
-            if attempt == max_attempts:
-                raise e
-            else:
-                print(f"Parsing error occurred. Retrying (attempt {attempt + 1}/{max_attempts})...")
-                attempt += 1
-
-    return None
-
-def display_extracted_fields(obj, section_title=None, indent=0):
-    if section_title:
-        st.subheader(section_title)
-    for field_name, field_value in obj:
-        if field_name in ["personal_details", "education", "work_experience", "projects", "skills", "certifications", "publications", "awards", "additional_sections"]:
-            st.write(" " * indent + f"**{field_name.replace('_', ' ').title()}**:")
-            if isinstance(field_value, BaseModel):
-                display_extracted_fields(field_value, None, indent + 1)
-            elif isinstance(field_value, list):
-                for item in field_value:
-                    if isinstance(item, BaseModel):
-                        display_extracted_fields(item, None, indent + 1)
-                    else:
-                        st.write(" " * (indent + 1) + "- " + str(item))
-            else:
-                st.write(" " * (indent + 1) + str(field_value))
-        else:
-            st.write(" " * indent + f"{field_name.replace('_', ' ').title()}: " + str(field_value))
-
-def get_json_download_link(json_str, download_name):
-    # Convert the JSON string back to a dictionary
-    data = json.loads(json_str)
-    
-    # Convert the dictionary back to a JSON string with 4 spaces indentation
-    json_str_formatted = json.dumps(data, indent=4)
-    
-    b64 = base64.b64encode(json_str_formatted.encode()).decode()
-    href = f'<a href="data:file/json;base64,{b64}" download="{download_name}.json">Click here to download the JSON file</a>'
-    return href
 
 st.set_page_config(layout="wide")
 
 st.title("Resume Parser")
+col1, col2 = st.columns([1,6])
+
+with col1:
+    st.image("llamallms.png", use_column_width=True)
+
+with col2:
+    st.write("""
+    ## 📝 Unlocking the Power of LLMs 🔓
+
+    Welcome to the Resume Parser, a powerful tool designed to extract structured information from resumes using the magic of Language Models (LLMs)! 🪄📄 As a data scientist and military veteran, I understand the importance of efficiency and accuracy when it comes to processing information. That's why I've created this app to showcase how different LLMs can help us parse resumes with ease. 💪
+
+    Resumes come in all shapes and sizes, and standardization is often a distant dream. 😴 But with the right LLM by your side, you can extract key information like personal details, education, work experience, and more, all with just a few clicks! 🖱️ Plus, by comparing the performance of various models, you can find the perfect balance of speed, accuracy, and cost for your specific use case. 💰
+
+    So, whether you're a recruiter looking to streamline your hiring process, or a data enthusiast curious about the capabilities of LLMs, the Resume Parser has got you covered! 🙌 Upload a resume, select your models, and watch the magic happen. 🎩✨ Let's unlock the full potential of LLMs together and make resume parsing a breeze! 😎
+    """)
 
 llm_dict = {
     "GPT 3.5 turbo": ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo"),
@@ -146,23 +71,23 @@ with col2:
     selected_model2 = st.selectbox("Select Model 2", list(llm_dict.keys()), index=list(llm_dict.keys()).index("Mixtral 8x7b"))
 
 if uploaded_file is not None:
-    text = pdf_to_string(uploaded_file)
+    text = resume_helpers.pdf_to_string(uploaded_file)
 
     if st.button("Extract Resume Fields"):
         col1, col2 = st.columns(2)
 
         with col1:
             start_time = time.time()
-            extracted_fields1 = extract_resume_fields(text, selected_model1)
+            extracted_fields1 = resume_helpers.extract_resume_fields(text, selected_model1)
             end_time = time.time()
             elapsed_time = end_time - start_time
             st.write(f"Extraction completed in {elapsed_time:.2f} seconds")
-            display_extracted_fields(extracted_fields1, f"{selected_model1} Extracted Fields ")
+            resume_helpers.display_extracted_fields(extracted_fields1, f"{selected_model1} Extracted Fields ")
 
         with col2:
             start_time = time.time()
-            extracted_fields2 = extract_resume_fields(text, selected_model2)
+            extracted_fields2 = resume_helpers.extract_resume_fields(text, selected_model2)
             end_time = time.time()
             elapsed_time = end_time - start_time
             st.write(f"Extraction completed in {elapsed_time:.2f} seconds")
-            display_extracted_fields(extracted_fields2, f"{selected_model2} Extracted Fields ")
+            resume_helpers.display_extracted_fields(extracted_fields2, f"{selected_model2} Extracted Fields ")

resume_helpers.py

@@ -0,0 +1,131 @@
+from dotenv import load_dotenv
+import io
+import streamlit as st
+import streamlit.components.v1 as components
+import base64
+
+from langchain.prompts import PromptTemplate
+from langchain_core.output_parsers import PydanticOutputParser
+from langchain_anthropic import ChatAnthropic
+from langchain_openai import ChatOpenAI
+from langchain_groq import ChatGroq
+from langchain_google_genai import ChatGoogleGenerativeAI
+from langchain_core.exceptions import OutputParserException
+from pydantic import ValidationError
+from langchain_core.pydantic_v1 import BaseModel, Field
+from resume_template import Resume
+from json import JSONDecodeError
+import PyPDF2
+import json
+import time
+import os
+
+
+# Set the LANGCHAIN_TRACING_V2 environment variable to 'true'
+os.environ['LANGCHAIN_TRACING_V2'] = 'true'
+
+# Set the LANGCHAIN_PROJECT environment variable to the desired project name
+os.environ['LANGCHAIN_PROJECT'] = 'Resume_Project'
+
+load_dotenv()
+llm_dict = {
+    "GPT 3.5 turbo": ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo"),
+    "Anthropic Sonnet": ChatAnthropic(model_name="claude-3-sonnet-20240229"),
+    "Llama 3 8b": ChatGroq(model_name="llama3-8b-8192"),
+    "Llama 3 70b": ChatGroq(model_name="llama3-70b-8192"),
+    "Gemma 7b": ChatGroq(model_name="gemma-7b-it"),
+    "Mixtral 8x7b": ChatGroq(model_name="mixtral-8x7b-32768"),
+    # "Gemini 1.5 Pro": ChatGoogleGenerativeAI(model_name="gemini-1.5-pro-latest"),
+}
+def pdf_to_string(file):
+    """
+    Convert a PDF file to a string.
+
+    Parameters:
+    file (io.BytesIO): A file-like object representing the PDF file.
+
+    Returns:
+    str: The extracted text from the PDF.
+    """
+    pdf_reader = PyPDF2.PdfReader(file)
+    num_pages = len(pdf_reader.pages)
+    text = ''
+    for i in range(num_pages):
+        page = pdf_reader.pages[i]
+        text += page.extract_text()
+    file.close()
+    return text
+
+class CustomOutputParserException(Exception):
+    pass
+
+def extract_resume_fields(full_text, model):
+    """
+    Analyze a resume text and extract structured information using a specified language model.
+    Parameters:
+    full_text (str): The text content of the resume.
+    model (str): The language model object to use for processing the text.
+    Returns:
+    dict: A dictionary containing structured information extracted from the resume.
+    """
+    # The Resume object is imported from the local resume_template file
+
+    with open("prompts/resume_extraction.prompt", "r") as f:
+        template = f.read()
+
+    parser = PydanticOutputParser(pydantic_object=Resume)
+
+    prompt_template = PromptTemplate(
+        template=template,
+        input_variables=["resume"],
+        partial_variables={"response_template": parser.get_format_instructions()},
+    )
+    llm = llm_dict.get(model, ChatOpenAI(temperature=0, model=model))
+
+    chain = prompt_template | llm | parser
+    max_attempts = 3
+    attempt = 1
+
+    while attempt <= max_attempts:
+        try:
+            output = chain.invoke(full_text)
+            print(output)
+            return output
+        except (CustomOutputParserException, ValidationError) as e:
+            if attempt == max_attempts:
+                raise e
+            else:
+                print(f"Parsing error occurred. Retrying (attempt {attempt + 1}/{max_attempts})...")
+                attempt += 1
+
+    return None
+
+def display_extracted_fields(obj, section_title=None, indent=0):
+    if section_title:
+        st.subheader(section_title)
+    for field_name, field_value in obj:
+        if field_name in ["personal_details", "education", "work_experience", "projects", "skills", "certifications", "publications", "awards", "additional_sections"]:
+            st.write(" " * indent + f"**{field_name.replace('_', ' ').title()}**:")
+            if isinstance(field_value, BaseModel):
+                display_extracted_fields(field_value, None, indent + 1)
+            elif isinstance(field_value, list):
+                for item in field_value:
+                    if isinstance(item, BaseModel):
+                        display_extracted_fields(item, None, indent + 1)
+                    else:
+                        st.write(" " * (indent + 1) + "- " + str(item))
+            else:
+                st.write(" " * (indent + 1) + str(field_value))
+        else:
+            st.write(" " * indent + f"{field_name.replace('_', ' ').title()}: " + str(field_value))
+
+def get_json_download_link(json_str, download_name):
+    # Convert the JSON string back to a dictionary
+    data = json.loads(json_str)
+    
+    # Convert the dictionary back to a JSON string with 4 spaces indentation
+    json_str_formatted = json.dumps(data, indent=4)
+    
+    b64 = base64.b64encode(json_str_formatted.encode()).decode()
+    href = f'<a href="data:file/json;base64,{b64}" download="{download_name}.json">Click here to download the JSON file</a>'
+    return href