Spaces:
Running
Running
File size: 4,321 Bytes
253e25a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 |
import gradio as gr
import json
import time
import os
from generator import PROFESSIONS_FILE, TYPES_FILE, OUTPUT_FILE
from generator import generate_questions, load_json_data, save_questions_to_file
from splitgpt import save_questions
# Load professions and interview types from JSON files
try:
professions_data = load_json_data(PROFESSIONS_FILE)
types_data = load_json_data(TYPES_FILE)
except (FileNotFoundError, json.JSONDecodeError) as e:
print(f"Error loading data from JSON files: {e}")
professions_data = []
types_data = []
# Extract profession names and interview types for the dropdown menus
profession_names = [item["profession"] for item in professions_data]
interview_types = [item["type"] for item in types_data]
# Define path for the questions.json file
QUESTIONS_FILE = "questions.json"
def generate_and_save_questions(profession, interview_type, num_questions, overwrite=True, progress=gr.Progress()):
"""
Generates questions using the generate_questions function and saves them to JSON files.
Provides progress updates.
"""
profession_info = next(
(item for item in professions_data if item["profession"] == profession), None
)
interview_type_info = next(
(item for item in types_data if item["type"] == interview_type), None
)
if profession_info is None or interview_type_info is None:
return "Error: Invalid profession or interview type selected.", None
description = profession_info["description"]
max_questions = min(int(num_questions), 20) # Ensure max is 20
progress(0, desc="Starting question generation...")
questions = generate_questions(
profession, interview_type, description, max_questions
)
progress(0.5, desc=f"Generated {len(questions)} questions. Saving...")
# Save the generated questions to the all_questions.json file
all_questions_entry = {
"profession": profession,
"interview_type": interview_type,
"description": description,
"max_questions": max_questions,
"questions": questions,
}
save_questions_to_file(OUTPUT_FILE, [all_questions_entry], overwrite=overwrite)
save_questions(questions)
# Save the generated questions to the new questions.json file
with open(QUESTIONS_FILE, "w") as outfile:
json.dump(questions, outfile, indent=4)
progress(1, desc="Questions saved.")
return (
f"✅ Questions generated and saved for {profession} ({interview_type}). Max questions: {max_questions}",
questions,
)
def update_max_questions(interview_type):
"""
Updates the default value of the number input based on the selected interview type.
"""
interview_type_info = next(
(item for item in types_data if item["type"] == interview_type), None
)
if interview_type_info:
default_max_questions = interview_type_info.get("max_questions", 5)
return gr.update(value=default_max_questions, minimum=1, maximum=20)
else:
return gr.update(value=5, minimum=1, maximum=20)
'''
with gr.Blocks() as demo:
gr.Markdown("## 📄 Interview Question Generator for IBM CIC")
with gr.Row():
profession_input = gr.Dropdown(label="Select Profession", choices=profession_names)
interview_type_input = gr.Dropdown(label="Select Interview Type", choices=interview_types)
num_questions_input = gr.Number(
label="Number of Questions (1-20)", value=5, precision=0, minimum=1, maximum=20
)
generate_button = gr.Button("Generate Questions")
output_text = gr.Textbox(label="Output")
question_output = gr.JSON(label="Generated Questions")
# Update num_questions_input when interview_type_input changes
interview_type_input.change(
fn=update_max_questions,
inputs=interview_type_input,
outputs=num_questions_input,
)
generate_button.click(
generate_and_save_questions,
inputs=[profession_input, interview_type_input, num_questions_input],
outputs=[output_text, question_output],
)
if __name__ == "__main__":
demo.queue().launch()
'''
|