Upload generatorgr.py

generatorgr.py

@@ -1,121 +1,125 @@
-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
-
-# 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 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()
+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()
+
+'''