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import streamlit as st
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
import datetime
import pandas as pd
# Adjust the system path to find project modules
current_dir = os.path.dirname(os.path.abspath(__file__))
project_root = os.path.dirname(os.path.dirname(os.path.dirname(current_dir)))
sys.path.append(project_root)
from src.modules.module2_relevancy.relevance_analyzer import EnhancedRelevanceAnalyzer
from groq_client import GroqClient
from file_processing import extract_text_from_file
from src.modules.module3_compare.model import QuestionSimilarityModel
from src.modules.module4_bias.bias import screen_questions
from src.modules.module1_question_generation.project_controller import Project
from src.modules.module1_question_generation.tool_controller import *
DATASET_DIR = "dataset"
project_control = Project()
if 'page' not in st.session_state:
st.session_state.page = 'main'
if ('accuracy_history' not in st.session_state):
st.session_state['accuracy_history'] = {
"DSA" : [],
"Technical" : [],
"Behaviour": []
}
def main():
if st.session_state.page == 'main':
sidebar()
if ('current_project' in st.session_state):
if (st.session_state['current_project']['project_name'] == 'default'):
st.title("Interview Question Generator & Analyzer")
main_page()
else:
st.subheader('No project selected')
elif st.session_state.page == 'configure':
configure_page()
def sidebar():
st.sidebar.title("Project Options")
project_action = st.sidebar.selectbox("Select Action", ["Open Existing Project", "Create New Project"])
if project_action == "Create New Project":
new_project_name = st.sidebar.text_input("Enter Project Name")
print('Title: ', new_project_name)
if st.sidebar.button("Create Project") and new_project_name:
if new_project_name in project_control.list_projects():
st.sidebar.error("Project with this name already exists.")
else:
project_data = project_control.initialize_project(new_project_name)
st.session_state["current_project"] = project_data
st.success(f"Project '{new_project_name}' created successfully!")
elif project_action == "Open Existing Project":
existing_projects = project_control.list_projects()
selected_project = st.sidebar.selectbox("Select Project", existing_projects)
if st.sidebar.button("Open Project") and selected_project:
project_data = project_control.load_project(selected_project)
if project_data:
st.session_state["current_project"] = project_data
else:
st.sidebar.error("Failed to load project_control.")
if ('current_project' in st.session_state and st.sidebar.button('Configure Project')):
st.session_state.page = 'configure'
def main_page():
client = GroqClient()
analyzer = EnhancedRelevanceAnalyzer()
similarity_model = QuestionSimilarityModel('dataset/leetcode_dataset.csv')
project = st.session_state["current_project"]
st.subheader('Project: ', project['project_name'])
job_role = st.text_input("Enter Job Role")
question_type = st.selectbox("Type of questions", ["DSA", "Technical", "Behaviour"])
jd_file = st.file_uploader("Upload Job Description (PDF/DOCX)", type=["pdf", "docx"])
if jd_file and job_role and question_type and st.button('Get questions') :
with st.spinner("Analyzing Job Description..."):
jd_text = extract_text_from_file(jd_file)
if not analyzer.check_title_jd_match(job_role, jd_text):
st.error("⚠️ Job description doesn't match the job title! Upload a relevant JD.")
st.stop()
questions = client.generate_questions(job_role, jd_text, question_type)
# Deterministic
d_results = verify_deterministic_assertions(questions, project["assertions"])
df_results = pd.DataFrame(list(d_results.items()), columns=["Assertion Type", "Result"])
st.table(df_results)
question_lines = [q.strip() for q in questions.split('\n') if q.strip()]
if question_lines and not question_lines[0][0].isdigit():
question_lines = question_lines[1:]
# first_five_questions = question_lines[:10]
# remaining_questions = question_lines[5:15]
scores = []
if (question_type == "DSA"):
similarity_results = similarity_model.check_similarity(question_lines)
scores = similarity_results
st.subheader("DSA questions with similarity analysis")
score = 0
for i, (question, result) in enumerate(zip(question_lines, similarity_results), 1):
st.write(f"{i}. {question}")
score += result["relevance_score"]
with st.expander(f"Similarity Analysis for Question {i}"):
st.write(f"Similarity Score: {result['relevance_score']:.2f}")
st.write(f"Best Match: {result['best_match']['title']}")
st.write(f"Difficulty: {result['best_match']['difficulty']}")
if result['matched_sources']:
st.write("\nSimilar Questions:")
for source in result['matched_sources']:
st.write(f"- {source['title']} (Difficulty: {source['difficulty']})")
overall_similarity = score / len(question_lines)
st.metric("Overall Relevance", f"{overall_similarity*100:.1f}%")
timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
project['accuracy_history'][question_type].append((timestamp, overall_similarity))
# if (question_type == "Technical" or question_type == "Behaviour"):
if (question_type == "Technical"):
for q in question_lines:
st.write(f"- {q}")
scores = analyzer.calculate_question_scores(jd_text, question_lines)
avg_score = sum(scores) / len(scores)
half_avg = avg_score / 1.25
count_above_half = sum(1 for s in scores if s > half_avg)
overall_relevance = (count_above_half / len(scores)) * 100
st.subheader("Analysis Results")
st.metric("Overall Relevance", f"{overall_relevance:.1f}%")
# Store accuracy with timestamp
timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
project['accuracy_history'][question_type].append((timestamp, overall_relevance))
if question_type == "Behaviour":
valid_bias_questions, invalid_bias_questions, bias_accuracy, validity = screen_questions(question_lines)
for i, q in enumerate(question_lines):
st.write(f"- {f'[Invalid {validity[i]:.2f}]' if validity[i] == 1 else f'[ Valid {validity[i]:.2f}]'} {q}")
st.metric("Bias Accuracy", f"{bias_accuracy * 100:.1f}%")
timestamp = datetime.datetime.now().strftime("%Y-%m-%d %H:%M:%S")
project['accuracy_history'][question_type].append((timestamp, bias_accuracy))
# Plot accuracy history
if project['accuracy_history']:
st.subheader("Accuracy History")
timestamps, accuracies = zip(*project['accuracy_history'][question_type])
fig, ax = plt.subplots()
ax.plot(timestamps, accuracies, marker='o')
ax.set_xlabel("Timestamp")
ax.set_ylabel("Overall Relevance (%)")
ax.set_title("Relevance Over Time")
plt.xticks(rotation=45)
st.pyplot(fig)
export_data = []
for i, (question, score) in enumerate(zip(question_lines, scores), 1):
export_data.append(f"Q{i}. {question}")
if (question_type == "DSA"):
export_data.append(f"Overall Score: {score['relevance_score']}")
export_data.append(f"Best Match: {score['best_match']['title']}")
else:
export_data.append(f"Overall Score: {score}")
export_data.append("")
# for i, (question, score) in enumerate(zip(remaining_questions, scores[5:15]), 5):
# export_data.append(f"Q{i}. {question}")
# export_data.append("")
project_control.save_project(project["project_name"], project)
st.download_button(
"Download Questions with Analysis",
f"Job Role: {job_role}\n\n\n" + "\n".join(export_data),
file_name=f"{job_role.replace(' ', '_')}_questions_analysis.txt",
mime="text/plain"
)
def configure_page():
st.title("Project Configuration")
project = st.session_state['current_project']
assertion_type = st.selectbox("Select Assertion Type", ["deterministic", "factual", "misc"])
if assertion_type == "deterministic":
check_type = st.selectbox("Select Deterministic Check Type", ["regex", "json_format", "contains", "not-contains"])
check_value = st.text_area("Enter pattern")
if st.button("Add Deterministic Assertion") and check_value:
assertion_data = {
"check_type": check_type,
"value": check_value,
}
project["assertions"]["deterministic"].append(assertion_data)
st.success("Deterministic Assertion added.")
elif assertion_type == "factual":
fact = st.file_uploader("Provide knowledgebase for factual assertion", type=["pdf", "docx"])
if st.button("Add") and fact:
project_id = project["project_name"]
file_extension = os.path.splitext(fact.name)[1]
# current working dir
saved_path = os.path.join(os.getcwd(), DATASET_DIR, f"{project_id}{file_extension}")
with open(saved_path, "wb") as f:
f.write(fact.getbuffer())
project["assertions"]["knowledgebase"] = saved_path
st.success("Factual Assertion added and file saved.")
elif assertion_type == "misc":
new_assertion = st.text_input("Add Miscellaneous Assertion")
if st.button("Add Miscellaneous Assertion") and new_assertion:
project["assertions"]["misc"].append(new_assertion)
if (st.checkbox('sql-only')):
project["assertions"]["sql-only"] = True
if (st.checkbox('json-only')):
project["assertions"]["json-only"] = True
if st.button("Save Assertion"):
project_control.save_project(project["project_name"], project)
st.success(f"Assertion saved")
if st.button("Go Back"):
st.session_state.page = 'main'
if __name__ == "__main__":
main()
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