Update app.py

app.py

@@ -1,11 +1,11 @@
+# app.py
+
 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 requests
 from bs4 import BeautifulSoup
-import uuid
 import plotly.express as px
 import re
 import pandas as pd
@@ -22,7 +22,6 @@ llm = ChatGroq(
     model_name="llama-3.1-70b-versatile"
 )
 
-
 def extract_text_from_pdf(pdf_file):
     """
     Extracts text from an uploaded PDF file.
@@ -48,6 +47,7 @@ def extract_job_description(job_link):
         response = requests.get(job_link, headers=headers)
         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:
@@ -58,7 +58,7 @@ def extract_requirements(job_description):
     """
     Uses Groq to extract job requirements from the job description.
     """
-    prompt_text = f"""
+    prompt = f"""
     The following is a job description:
 
     {job_description}
@@ -68,18 +68,19 @@ def extract_requirements(job_description):
     Requirements:
     """
 
-    prompt = PromptTemplate.from_template(prompt_text)
-    chain = prompt | llm
-    response = chain.invoke(prompt)  # Changed from dict to string
-
-    requirements = response.content.strip()
-    return requirements
+    try:
+        response = llm.invoke(prompt)
+        requirements = response.content.strip()
+        return requirements
+    except Exception as e:
+        st.error(f"Error extracting requirements: {e}")
+        return ""
 
 def generate_email(job_description, requirements, resume_text):
     """
     Generates a personalized cold email using Groq based on the job description, requirements, and resume.
     """
-    prompt_text = f"""
+    prompt = f"""
     You are Adithya S Nair, a recent Computer Science graduate specializing in Artificial Intelligence and Machine Learning. Craft a concise and professional cold email to a potential employer based on the following information:
 
     **Job Description:**
@@ -100,18 +101,19 @@ def generate_email(job_description, requirements, resume_text):
     **Email:**
     """
 
-    prompt = PromptTemplate.from_template(prompt_text)
-    chain = prompt | llm
-    response = chain.invoke(prompt)  # Changed from dict to string
-
-    email_text = response.content.strip()
-    return email_text
+    try:
+        response = llm.invoke(prompt)
+        email_text = response.content.strip()
+        return email_text
+    except Exception as e:
+        st.error(f"Error generating email: {e}")
+        return ""
 
 def generate_cover_letter(job_description, requirements, resume_text):
     """
     Generates a personalized cover letter using Groq based on the job description, requirements, and resume.
     """
-    prompt_text = f"""
+    prompt = f"""
     You are Adithya S Nair, a recent Computer Science graduate specializing in Artificial Intelligence and Machine Learning. Compose a personalized and professional cover letter based on the following information:
 
     **Job Description:**
@@ -136,18 +138,19 @@ def generate_cover_letter(job_description, requirements, resume_text):
     **Cover Letter:**
     """
 
-    prompt = PromptTemplate.from_template(prompt_text)
-    chain = prompt | llm
-    response = chain.invoke(prompt)  # Changed from dict to string
-
-    cover_letter = response.content.strip()
-    return cover_letter
+    try:
+        response = llm.invoke(prompt)
+        cover_letter = response.content.strip()
+        return cover_letter
+    except Exception as e:
+        st.error(f"Error generating cover letter: {e}")
+        return ""
 
 def extract_skills(text):
     """
     Extracts a list of skills from the resume text using Groq.
     """
-    prompt_text = f"""
+    prompt = f"""
     Extract a comprehensive list of technical and soft skills from the following resume text. Provide the skills as a comma-separated list.
 
     Resume Text:
@@ -156,20 +159,21 @@ def extract_skills(text):
     Skills:
     """
 
-    prompt = PromptTemplate.from_template(prompt_text)
-    chain = prompt | llm
-    response = chain.invoke(prompt)  # Changed from dict to string
-
-    skills = response.content.strip()
-    # Clean and split the skills
-    skills_list = [skill.strip() for skill in re.split(',|\n', skills) if skill.strip()]
-    return skills_list
+    try:
+        response = llm.invoke(prompt)
+        skills = response.content.strip()
+        # Clean and split the skills
+        skills_list = [skill.strip() for skill in re.split(',|\n', skills) if skill.strip()]
+        return skills_list
+    except Exception as e:
+        st.error(f"Error extracting skills: {e}")
+        return []
 
 def suggest_keywords(resume_text, job_description=None):
     """
     Suggests additional relevant keywords to enhance resume compatibility with ATS.
     """
-    prompt_text = f"""
+    prompt = f"""
     Analyze the following resume text and suggest additional relevant keywords that can enhance its compatibility with Applicant Tracking Systems (ATS). If a job description is provided, tailor the keywords to align with the job requirements.
 
     Resume Text:
@@ -181,13 +185,14 @@ def suggest_keywords(resume_text, job_description=None):
     Suggested Keywords:
     """
 
-    prompt = PromptTemplate.from_template(prompt_text)
-    chain = prompt | llm
-    response = chain.invoke(prompt)  # Changed from dict to string
-
-    keywords = response.content.strip()
-    keywords_list = [keyword.strip() for keyword in re.split(',|\n', keywords) if keyword.strip()]
-    return keywords_list
+    try:
+        response = llm.invoke(prompt)
+        keywords = response.content.strip()
+        keywords_list = [keyword.strip() for keyword in re.split(',|\n', keywords) if keyword.strip()]
+        return keywords_list
+    except Exception as e:
+        st.error(f"Error suggesting keywords: {e}")
+        return []
 
 def get_job_recommendations(job_title, location="India"):
     """
@@ -195,8 +200,8 @@ def get_job_recommendations(job_title, location="India"):
     """
     url = "https://jsearch.p.rapidapi.com/estimated-salary"
     querystring = {
-        "job_title": job_title,
-        "location": location,
+        "job_title": job_title.strip(),
+        "location": location.strip(),
         "radius": "100"  # Adjust radius as needed
     }
 
@@ -220,6 +225,9 @@ def get_job_recommendations(job_title, location="India"):
             "avg_salary": avg_salary,
             "max_salary": max_salary
         }
+    except requests.exceptions.HTTPError as http_err:
+        st.error(f"HTTP error occurred: {http_err}")
+        return {}
     except Exception as e:
         st.error(f"Error fetching salary data: {e}")
         return {}
@@ -240,7 +248,7 @@ def create_experience_timeline(resume_text):
     Creates an experience timeline from the resume text.
     """
     # Extract work experience details using Groq
-    prompt_text = f"""
+    prompt = f"""
     From the following resume text, extract the job titles, companies, and durations of employment. Provide the information in a table format with columns: Job Title, Company, Duration (in years).
 
     Resume Text:
@@ -249,32 +257,33 @@ def create_experience_timeline(resume_text):
     Table:
     """
 
-    prompt = PromptTemplate.from_template(prompt_text)
-    chain = prompt | llm
-    response = chain.invoke(prompt)  # Changed from dict to string
-
-    table_text = response.content.strip()
-    # Parse the table_text to create a DataFrame
-    data = []
-    for line in table_text.split('\n'):
-        if line.strip() and not line.lower().startswith("job title"):
-            parts = line.split('|')
-            if len(parts) == 3:
-                job_title = parts[0].strip()
-                company = parts[1].strip()
-                duration = parts[2].strip()
-                # Convert duration to a float representing years
-                duration_years = parse_duration(duration)
-                data.append({"Job Title": job_title, "Company": company, "Duration (years)": duration_years})
-    df = pd.DataFrame(data)
-    if not df.empty:
-        # Create a cumulative duration for timeline
-        df['Start Year'] = df['Duration (years)'].cumsum() - df['Duration (years)']
-        df['End Year'] = df['Duration (years)'].cumsum()
-        fig = px.timeline(df, x_start="Start Year", x_end="End Year", y="Job Title", color="Company", title="Experience Timeline")
-        fig.update_yaxes(categoryorder="total ascending")
-        return fig
-    else:
+    try:
+        response = llm.invoke(prompt)
+        table_text = response.content.strip()
+        # Parse the table_text to create a DataFrame
+        data = []
+        for line in table_text.split('\n'):
+            if line.strip() and not line.lower().startswith("job title"):
+                parts = line.split('|')
+                if len(parts) == 3:
+                    job_title = parts[0].strip()
+                    company = parts[1].strip()
+                    duration = parts[2].strip()
+                    # Convert duration to a float representing years
+                    duration_years = parse_duration(duration)
+                    data.append({"Job Title": job_title, "Company": company, "Duration (years)": duration_years})
+        df = pd.DataFrame(data)
+        if not df.empty:
+            # Create a cumulative duration for timeline
+            df['Start Year'] = df['Duration (years)'].cumsum() - df['Duration (years)']
+            df['End Year'] = df['Duration (years)'].cumsum()
+            fig = px.timeline(df, x_start="Start Year", x_end="End Year", y="Job Title", color="Company", title="Experience Timeline")
+            fig.update_yaxes(categoryorder="total ascending")
+            return fig
+        else:
+            return None
+    except Exception as e:
+        st.error(f"Error creating experience timeline: {e}")
         return None
 
 def parse_duration(duration_str):
@@ -350,7 +359,7 @@ def fetch_applications():
             "Notes": app[6],
             "Job Description": app[7],
             "Resume Text": app[8],
-            "Skills": app[9].split(', ')
+            "Skills": app[9].split(', ') if app[9] else []
         })
     return applications
 
@@ -390,9 +399,13 @@ def generate_learning_path(career_goal, current_skills):
     **Learning Path:**
     """
 
-    response = llm.invoke(prompt)  # Changed from dict to string
-    learning_path = response.content.strip()
-    return learning_path
+    try:
+        response = llm.invoke(prompt)
+        learning_path = response.content.strip()
+        return learning_path
+    except Exception as e:
+        st.error(f"Error generating learning path: {e}")
+        return ""
 
 # -------------------------------
 # Page Functions
@@ -517,13 +530,16 @@ def resume_analysis_page():
             st.subheader("Extracted Information")
             # Extracted skills
             skills = extract_skills(resume_text)
-            st.write("**Skills:**", ', '.join(skills))
+            st.write("**Skills:**", ', '.join(skills) if skills else "No skills extracted.")
             # Extract keywords
             keywords = suggest_keywords(resume_text)
-            st.write("**Suggested Keywords for ATS Optimization:**", ', '.join(keywords))
+            st.write("**Suggested Keywords for ATS Optimization:**", ', '.join(keywords) if keywords else "No keywords suggested.")
             # Provide optimization suggestions
             st.subheader("Optimization Suggestions")
-            st.write("- **Keyword Optimization:** Incorporate the suggested keywords to improve ATS compatibility.")
+            if keywords:
+                st.write("- **Keyword Optimization:** Incorporate the suggested keywords to improve ATS compatibility.")
+            else:
+                st.write("- **Keyword Optimization:** No keywords suggested.")
             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.")
 
@@ -534,6 +550,8 @@ def resume_analysis_page():
                 st.write("**Skill Distribution:**")
                 fig_skills = create_skill_distribution_chart(skills)
                 st.plotly_chart(fig_skills)
+            else:
+                st.write("**Skill Distribution:** No skills to display.")
 
             # Experience Timeline (if applicable)
             fig_experience = create_experience_timeline(resume_text)
@@ -653,23 +671,29 @@ def interview_preparation_module():
             prompt = f"""
             Generate a list of 10 interview questions for a {job_title} position at {company}. Include a mix of technical and behavioral questions.
             """
-            questions = llm.invoke(prompt).content.strip()  # Changed from dict to string
-            st.subheader("Mock Interview Questions:")
-            st.write(questions)
-
-            # Optionally, provide sample answers or tips
-            if st.checkbox("Show Sample Answers"):
-                sample_prompt = f"""
-                Provide sample answers for the following interview questions for a {job_title} position at {company}.
-
-                Questions:
-                {questions}
-
-                Sample Answers:
-                """
-                sample_answers = llm.invoke(sample_prompt).content.strip()  # Changed from dict to string
-                st.subheader("Sample Answers:")
-                st.write(sample_answers)
+            try:
+                questions = llm.invoke(prompt).content.strip()
+                st.subheader("Mock Interview Questions:")
+                st.write(questions)
+
+                # Optionally, provide sample answers or tips
+                if st.checkbox("Show Sample Answers"):
+                    sample_prompt = f"""
+                    Provide sample answers for the following interview questions for a {job_title} position at {company}.
+
+                    Questions:
+                    {questions}
+
+                    Sample Answers:
+                    """
+                    try:
+                        sample_answers = llm.invoke(sample_prompt).content.strip()
+                        st.subheader("Sample Answers:")
+                        st.write(sample_answers)
+                    except Exception as e:
+                        st.error(f"Error generating sample answers: {e}")
+            except Exception as e:
+                st.error(f"Error generating interview questions: {e}")
 
 def personalized_learning_paths_module():
     st.header("Personalized Learning Paths")
@@ -688,8 +712,11 @@ def personalized_learning_paths_module():
             return
         with st.spinner("Generating your personalized learning path..."):
             learning_path = generate_learning_path(career_goal, current_skills)
-            st.subheader("Your Personalized Learning Path:")
-            st.write(learning_path)
+            if learning_path:
+                st.subheader("Your Personalized Learning Path:")
+                st.write(learning_path)
+            else:
+                st.error("Failed to generate learning path.")
 
 def networking_opportunities_module():
     st.header("Networking Opportunities")
@@ -710,9 +737,12 @@ def networking_opportunities_module():
             prompt = f"""
             Based on the following skills: {user_skills}, and industry: {industry}, suggest relevant LinkedIn groups, professional organizations, and industry events for networking.
             """
-            suggestions = llm.invoke(prompt).content.strip()  # Changed from dict to string
-            st.subheader("Recommended Networking Groups and Events:")
-            st.write(suggestions)
+            try:
+                suggestions = llm.invoke(prompt).content.strip()
+                st.subheader("Recommended Networking Groups and Events:")
+                st.write(suggestions)
+            except Exception as e:
+                st.error(f"Error fetching networking opportunities: {e}")
 
 def salary_estimation_module():
     st.header("Salary Estimation and Negotiation Tips")
@@ -761,9 +791,12 @@ def salary_estimation_module():
                 tips_prompt = f"""
                 Provide a list of 5 effective tips for negotiating a salary for a {job_title} position in {location}.
                 """
-                tips = llm.invoke(tips_prompt).content.strip()  # Changed from dict to string
-                st.subheader("Negotiation Tips:")
-                st.write(tips)
+                try:
+                    tips = llm.invoke(tips_prompt).content.strip()
+                    st.subheader("Negotiation Tips:")
+                    st.write(tips)
+                except Exception as e:
+                    st.error(f"Error generating negotiation tips: {e}")
             else:
                 st.error("Failed to retrieve salary data.")
 
@@ -797,6 +830,9 @@ def gamification_module():
     Stay motivated by earning badges and tracking your progress!
     """)
 
+    # Initialize database
+    init_db()
+
     # Example achievements
     applications = fetch_applications()
     num_apps = len(applications)
@@ -815,10 +851,10 @@ def gamification_module():
             st.info(f"🔜 {achievement}")
 
     # Progress Bar
-    progress = min(num_apps / 10, 1.0)  # Changed from num_apps / 10 * 100 to num_apps / 10
+    progress = min(num_apps / 10, 1.0)  # Ensure progress is between 0.0 and 1.0
     st.write("**Overall Progress:**")
     st.progress(progress)
-    st.write(f"{progress * 100:.0f}% complete")  # Adjusted percentage display
+    st.write(f"{progress * 100:.0f}% complete")
 
 def resource_library_page():
     st.header("Resource Library")
@@ -857,7 +893,7 @@ def resource_library_page():
                     mime="application/octet-stream"
                 )
         except FileNotFoundError:
-            st.error(f"File {resource['file']} not found.")
+            st.error(f"File {resource['file']} not found. Please ensure the file is in the correct directory.")
         st.write("---")
 
 def success_stories_page():
@@ -917,8 +953,12 @@ def chatbot_support_page():
 
             {user_input}
             """
-            response = llm.invoke(prompt).content.strip()  # Changed from dict to string
-            st.session_state['chat_history'].append(f"Assistant: {response}")
+            try:
+                response = llm.invoke(prompt).content.strip()
+                st.session_state['chat_history'].append(f"Assistant: {response}")
+            except Exception as e:
+                st.session_state['chat_history'].append(f"Assistant: Sorry, I encountered an error while processing your request.")
+                st.error(f"Error in chatbot: {e}")
 
     # Display chat history
     for message in st.session_state['chat_history']:
@@ -934,6 +974,9 @@ def chatbot_support_page():
 def main():
     st.set_page_config(page_title="Job Application Assistant", layout="wide")
 
+    # Initialize database early to ensure tables exist
+    init_db()
+
     # Sidebar Navigation
     with st.sidebar:
         selected = option_menu(