Update app.py

app.py

@@ -1,38 +1,42 @@
 import streamlit as st
+from streamlit_option_menu import option_menu
 from langchain_groq import ChatGroq
 from langchain_core.prompts import PromptTemplate
-import fitz  # PyMuPDF
+import fitz 
 import requests
 from bs4 import BeautifulSoup
+import uuid
 
-# Initialize the LLM with your Groq API key
 llm = ChatGroq(
     temperature=0,
     groq_api_key='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',  # Replace with your Groq API key
     model_name="llama-3.1-70b-versatile"
 )
 
-# Function to extract text from a PDF file
+
 def extract_text_from_pdf(pdf_file):
     text = ""
-    with fitz.open(stream=pdf_file.read(), filetype="pdf") as doc:
-        for page in doc:
-            text += page.get_text()
-    return text
+    try:
+        with fitz.open(stream=pdf_file.read(), filetype="pdf") as doc:
+            for page in doc:
+                text += page.get_text()
+        return text
+    except Exception as e:
+        st.error(f"Error extracting text from resume: {e}")
+        return ""
 
-# Function to extract the job description from a given URL
 def extract_job_description(job_link):
     try:
         response = requests.get(job_link)
         response.raise_for_status()
         soup = BeautifulSoup(response.text, 'html.parser')
+        # You might need to adjust the selectors based on the website's structure
         job_description = soup.get_text(separator='\n')
         return job_description.strip()
     except Exception as e:
         st.error(f"Error fetching job description: {e}")
         return ""
 
-# Function to extract requirements from the job description using ChatGroq
 def extract_requirements(job_description):
     prompt_text = f"""
     The following is a job description:
@@ -51,7 +55,6 @@ def extract_requirements(job_description):
     requirements = response.content.strip()
     return requirements
 
-# Function to generate an email using ChatGroq
 def generate_email(job_description, requirements, resume_text):
     prompt_text = f"""
     Given the following job description:
@@ -63,10 +66,12 @@ And the following extracted requirements:
 And the following resume text:
 {resume_text}
 
-Write a cold email as Adithya S Nair, a recent graduate in Computer Science with a focus on Artificial Intelligence and Machine Learning. Highlight your relevant skills and experiences from the resume, emphasizing how you, as a fresher, can bring value to the client’s company. Mention key projects, internships, and any leadership experiences that align with the job description and requirements. Ensure the email is concise and professional.
-
-Email:
+Write a cold email as Adithya S Nair, a recent graduate in Computer Science with a focus on Artificial Intelligence and Machine Learning. 
+Highlight your relevant skills and experiences from the resume, emphasizing how you, as a fresher, can bring value to the client’s company. 
+Mention key projects, internships, and any leadership experiences that align with the job description and requirements. 
+Ensure the email is concise and professional.
 
+    Email:
     """
 
     prompt = PromptTemplate.from_template(prompt_text)
@@ -76,9 +81,12 @@ Email:
     email_text = response.content.strip()
     return email_text
 
-# Streamlit App
-def main():
-    st.title("Automated Email Generator")
+# -------------------------------
+# Page Functions
+# -------------------------------
+
+def email_generator_page():
+    st.header("Automated Email Generator")
 
     st.write("""
     This application generates a personalized email based on a job posting and your resume.
@@ -123,5 +131,84 @@ def main():
             else:
                 st.error("Failed to generate email.")
 
+def resume_analysis_page():
+    st.header("Resume Analysis and Optimization")
+
+    uploaded_file = st.file_uploader("Upload your resume (PDF format):", type="pdf")
+
+    if uploaded_file:
+        resume_text = extract_text_from_pdf(uploaded_file)
+        if resume_text:
+            st.success("Resume uploaded successfully!")
+            # Perform analysis
+            st.subheader("Extracted Information")
+            # Example: Extracted skills
+            skills = extract_skills(resume_text)
+            st.write("**Skills:**", ', '.join(skills))
+            # Provide optimization suggestions
+            st.subheader("Optimization Suggestions")
+            st.write("- **Keyword Optimization:** Consider adding more industry-specific keywords relevant to your desired roles.")
+            st.write("- **Formatting:** Ensure consistent formatting for headings and bullet points to enhance readability.")
+            st.write("- **Experience Details:** Provide specific achievements and quantify your accomplishments where possible.")
+        else:
+            st.error("Failed to extract text from resume.")
+
+def job_recommendations_page():
+    st.header("Job Recommendations")
+
+    uploaded_file = st.file_uploader("Upload your resume (PDF format):", type="pdf")
+
+    if uploaded_file:
+        resume_text = extract_text_from_pdf(uploaded_file)
+        if resume_text:
+            st.success("Resume uploaded successfully!")
+            # Fetch job recommendations
+            st.subheader("Recommended Jobs")
+            jobs = get_job_recommendations(resume_text)
+            for job in jobs:
+                st.write(f"**{job['title']}** at {job['company']}")
+                st.markdown(f"[Apply Here]({job['link']})")
+        else:
+            st.error("Failed to extract text from resume.")
+
+# Placeholder for job recommendations - Replace with actual implementation
+def get_job_recommendations(resume_text):
+    # Implement job fetching logic, possibly integrating with job APIs
+    # This is a placeholder example
+    return [
+        {"title": "Data Scientist", "company": "TechCorp", "link": "https://example.com/job1"},
+        {"title": "Machine Learning Engineer", "company": "InnovateX", "link": "https://example.com/job2"},
+    ]
+
+def extract_skills(text):
+    # Implement advanced skill extraction logic
+    # For demonstration, using a predefined skill list
+    skills_list = ["Python", "Machine Learning", "Data Analysis", "SQL", "Communication", "Leadership"]
+    extracted_skills = [skill for skill in skills_list if skill.lower() in text.lower()]
+    return extracted_skills
+
+# -------------------------------
+# Main App with Sidebar Navigation
+# -------------------------------
+
+def main():
+    st.set_page_config(page_title="Job Application Assistant", layout="wide")
+
+    with st.sidebar:
+        selected = option_menu(
+            "Main Menu",
+            ["Email Generator", "Resume Analysis", "Job Recommendations"],
+            icons=["envelope", "file-person", "briefcase"],
+            menu_icon="cast",
+            default_index=0,
+        )
+
+    if selected == "Email Generator":
+        email_generator_page()
+    elif selected == "Resume Analysis":
+        resume_analysis_page()
+    elif selected == "Job Recommendations":
+        job_recommendations_page()
+
 if __name__ == "__main__":
     main()