# PrepGenie/app.py """Main Gradio application file.""" import gradio as gr import os import json import google.generativeai as genai from dotenv import load_dotenv import datetime # --- Environment and Configuration --- load_dotenv() # --- Generative AI Setup --- genai.configure(api_key=os.getenv("GOOGLE_API_KEY") or "YOUR_DEFAULT_API_KEY_HERE") TEXT_MODEL = genai.GenerativeModel("gemini-1.5-flash") # Global model instance print("Using Generative AI model: gemini-1.5-flash") # --- Import Logic Modules --- import interview_logic import interview_history # --- Helper Functions for UI Updates --- def apply_ui_updates(updates_dict): """Converts logic function UI update instructions to Gradio updates.""" gr_updates = {} for component_name, instruction in updates_dict.items(): if instruction == "gr_hide": gr_updates[component_name] = gr.update(visible=False) elif instruction == "gr_show": gr_updates[component_name] = gr.update(visible=True) elif instruction == "gr_show_and_update": gr_updates[component_name] = gr.update(visible=True) elif instruction == "gr_show_and_update_error": gr_updates[component_name] = gr.update(visible=True) elif instruction == "gr_clear": gr_updates[component_name] = "" elif instruction == "gr_clear_dict": gr_updates[component_name] = {} else: gr_updates[component_name] = gr.update() return gr_updates # --- Navigation Functions --- def navigate_to_interview(): return (gr.update(visible=True), gr.update(visible=False), gr.update(visible=False)) def navigate_to_chat(): return (gr.update(visible=False), gr.update(visible=True), gr.update(visible=False)) def navigate_to_history(): return (gr.update(visible=False), gr.update(visible=False), gr.update(visible=True)) # --- Event Handler Functions --- def process_resume_handler(file_obj): result = interview_logic.process_resume_logic(file_obj) ui_updates = apply_ui_updates(result["ui_updates"]) return ( result["status"], ui_updates.get("role_selection", gr.update()), ui_updates.get("start_interview_btn", gr.update()), ui_updates.get("question_display", gr.update()), ui_updates.get("answer_instructions", gr.update()), ui_updates.get("audio_input", gr.update()), ui_updates.get("submit_answer_btn", gr.update()), ui_updates.get("next_question_btn", gr.update()), ui_updates.get("submit_interview_btn", gr.update()), ui_updates.get("answer_display", gr.update()), ui_updates.get("feedback_display", gr.update()), ui_updates.get("metrics_display", gr.update()), result["processed_data"] ) def start_interview_handler(roles, processed_resume_data): formatted_resume_data = interview_logic.getallinfo(processed_resume_data, TEXT_MODEL) result = interview_logic.start_interview_logic(roles, formatted_resume_data, TEXT_MODEL) ui_updates = apply_ui_updates(result["ui_updates"]) return ( result["status"], result["initial_question"], ui_updates.get("audio_input", gr.update()), ui_updates.get("submit_answer_btn", gr.update()), ui_updates.get("next_question_btn", gr.update()), ui_updates.get("submit_interview_btn", gr.update()), ui_updates.get("feedback_display", gr.update()), ui_updates.get("metrics_display", gr.update()), ui_updates.get("question_display", gr.update()), ui_updates.get("answer_instructions", gr.update()), result["interview_state"] ) def submit_answer_handler(audio, interview_state): result = interview_logic.submit_answer_logic(audio, interview_state, TEXT_MODEL) ui_updates = apply_ui_updates(result["ui_updates"]) feedback_update = ui_updates.get("feedback_display", gr.update()) if "gr_show_and_update" in result["ui_updates"].values(): feedback_update = gr.update(visible=True, value=result["feedback_text"]) metrics_update = ui_updates.get("metrics_display", gr.update()) if "gr_show_and_update" in result["ui_updates"].values(): metrics_update = gr.update(visible=True, value=result["metrics"]) return ( result["status"], result["answer_text"], result["interview_state"], feedback_update, metrics_update, ui_updates.get("audio_input", gr.update()), ui_updates.get("submit_answer_btn", gr.update()), ui_updates.get("next_question_btn", gr.update()), ui_updates.get("submit_interview_btn", gr.update()), ui_updates.get("question_display", gr.update()), ui_updates.get("answer_instructions", gr.update()) ) def next_question_handler(interview_state): result = interview_logic.next_question_logic(interview_state) ui_updates = apply_ui_updates(result["ui_updates"]) return ( result["status"], result["next_q"], result["interview_state"], ui_updates.get("audio_input", gr.update()), ui_updates.get("submit_answer_btn", gr.update()), ui_updates.get("next_question_btn", gr.update()), ui_updates.get("feedback_display", gr.update()), ui_updates.get("metrics_display", gr.update()), ui_updates.get("submit_interview_btn", gr.update()), ui_updates.get("question_display", gr.update()), ui_updates.get("answer_instructions", gr.update()), ui_updates.get("answer_display", ""), ui_updates.get("metrics_display_clear", {}) ) def submit_interview_handler(interview_state): result = interview_logic.submit_interview_logic(interview_state, TEXT_MODEL) ui_updates = apply_ui_updates(result["ui_updates"]) report_update = ui_updates.get("evaluation_report_display", gr.update()) if "gr_show_and_update" in result["ui_updates"].values(): report_update = gr.update(visible=True, value=result["report_text"]) elif "gr_show_and_update_error" in result["ui_updates"].values(): report_update = gr.update(visible=True, value=result["report_text"]) chart_update = ui_updates.get("evaluation_chart_display", gr.update()) if "gr_show_and_update" in result["ui_updates"].values(): chart_update = gr.update(visible=True, value=result["chart_buffer"]) elif "gr_show_and_update_error" in result["ui_updates"].values(): chart_update = gr.update(visible=False) return ( result["status"], result["interview_state"], report_update, chart_update ) # --- Chat Module Functions --- try: from login_module import chat as chat_module CHAT_MODULE_AVAILABLE = True print("Chat module imported successfully.") except ImportError as e: print(f"Warning: Could not import chat module: {e}") CHAT_MODULE_AVAILABLE = False chat_module = None # --- Gradio Interface --- with gr.Blocks(title="PrepGenie - Mock Interviewer") as demo: # --- State Variables --- interview_state = gr.State({}) interview_history_state = gr.State([]) processed_resume_data_state = gr.State("") # --- Header --- with gr.Row(): gr.Markdown( """

PrepGenie- Interview Preparation App

""", elem_id="title" ) # --- Main App --- with gr.Column(visible=True) as main_app: with gr.Row(): # --- Navigation Column (Left) --- with gr.Column(scale=1): mock_interview_btn = gr.Button("Mock Interview") if CHAT_MODULE_AVAILABLE: chat_btn = gr.Button("Chat with Resume") else: chat_btn = gr.Button("Chat with Resume (Unavailable)", interactive=False) history_btn = gr.Button("My Interview History") # --- Content Column (Right) --- with gr.Column(scale=4): # --- Interview Section --- with gr.Column(visible=True) as interview_selection: gr.Markdown("## Mock Interview") with gr.Row(): with gr.Column(): file_upload_interview = gr.File(label="Upload Resume (PDF)", file_types=[".pdf"]) process_btn_interview = gr.Button("Process Resume") with gr.Column(): file_status_interview = gr.Textbox(label="Status", interactive=False) role_selection = gr.Dropdown( choices=["Data Scientist", "Software Engineer", "Product Manager", "Data Analyst", "Business Analyst"], multiselect=True, label="Select Job Role(s)", visible=False ) start_interview_btn = gr.Button("Start Interview", visible=False) question_display = gr.Textbox(label="Question", interactive=False, visible=False) answer_instructions = gr.Markdown("Click 'Record Answer' and speak your response.", visible=False) audio_input = gr.Audio(label="Record Answer", type="numpy", visible=False) submit_answer_btn = gr.Button("Submit Answer", visible=False) next_question_btn = gr.Button("Next Question", visible=False) submit_interview_btn = gr.Button("Submit Interview", visible=False, variant="primary") answer_display = gr.Textbox(label="Your Answer", interactive=False, visible=False) feedback_display = gr.Textbox(label="Feedback", interactive=False, visible=False) metrics_display = gr.JSON(label="Metrics", visible=False) processed_resume_data_hidden_interview = gr.Textbox(visible=False) with gr.Column(visible=False) as evaluation_selection: gr.Markdown("## Interview Evaluation Results") evaluation_report_display = gr.Markdown(label="Your Evaluation Report", visible=False) evaluation_chart_display = gr.Image(label="Skills Breakdown", type="pil", visible=False) # --- Chat Section --- with gr.Column(visible=False) as chat_selection: if CHAT_MODULE_AVAILABLE: gr.Markdown("## Chat with Resume") with gr.Row(): with gr.Column(): file_upload_chat = gr.File(label="Upload Resume (PDF)", file_types=[".pdf"]) process_chat_btn = gr.Button("Process Resume") with gr.Column(): file_status_chat = gr.Textbox(label="Status", interactive=False) chatbot = gr.Chatbot(label="Chat History", visible=False, type="messages") query_input = gr.Textbox(label="Ask about your resume", placeholder="Type your question here...", visible=False) send_btn = gr.Button("Send", visible=False) else: gr.Markdown("## Chat with Resume (Unavailable)") gr.Textbox(value="Chat module is not available.", interactive=False) # --- History Section --- with gr.Column(visible=False) as history_selection: gr.Markdown("## Your Interview History") load_history_btn = gr.Button("Load My Past Interviews") history_output = gr.Textbox(label="Past Interviews", max_lines=30, interactive=False, visible=True) # --- Event Listeners for Navigation --- mock_interview_btn.click( fn=navigate_to_interview, inputs=None, outputs=[interview_selection, chat_selection, history_selection] ) if CHAT_MODULE_AVAILABLE: chat_btn.click( fn=navigate_to_chat, inputs=None, outputs=[interview_selection, chat_selection, history_selection] ) history_btn.click( fn=navigate_to_history, inputs=None, outputs=[interview_selection, chat_selection, history_selection] ) # --- Event Listeners for Interview --- process_btn_interview.click( fn=process_resume_handler, inputs=[file_upload_interview], outputs=[ file_status_interview, role_selection, start_interview_btn, question_display, answer_instructions, audio_input, submit_answer_btn, next_question_btn, submit_interview_btn, answer_display, feedback_display, metrics_display, processed_resume_data_hidden_interview ] ) start_interview_btn.click( fn=start_interview_handler, inputs=[role_selection, processed_resume_data_hidden_interview], outputs=[ file_status_interview, question_display, audio_input, submit_answer_btn, next_question_btn, submit_interview_btn, feedback_display, metrics_display, question_display, answer_instructions, interview_state ] ) submit_answer_btn.click( fn=submit_answer_handler, inputs=[audio_input, interview_state], outputs=[ file_status_interview, answer_display, interview_state, feedback_display, metrics_display, audio_input, submit_answer_btn, next_question_btn, submit_interview_btn, question_display, answer_instructions ] ) next_question_btn.click( fn=next_question_handler, inputs=[interview_state], outputs=[ file_status_interview, question_display, interview_state, audio_input, submit_answer_btn, next_question_btn, feedback_display, metrics_display, submit_interview_btn, question_display, answer_instructions, answer_display, metrics_display ] ) submit_interview_btn.click( fn=submit_interview_handler, inputs=[interview_state], outputs=[ file_status_interview, interview_state, evaluation_report_display, evaluation_chart_display ] ) # --- Event Listeners for Chat --- if CHAT_MODULE_AVAILABLE: process_chat_btn.click( fn=chat_module.process_resume_chat, inputs=[file_upload_chat], outputs=[file_status_chat, processed_resume_data_state, query_input, send_btn, chatbot] ) send_btn.click( fn=chat_module.chat_with_resume, inputs=[query_input, processed_resume_data_state, chatbot], outputs=[query_input, chatbot] ) query_input.submit( fn=chat_module.chat_with_resume, inputs=[query_input, processed_resume_data_state, chatbot], outputs=[query_input, chatbot] ) # --- Event Listener for History --- def load_user_history_local(interview_history_state): if not interview_history_state: return "No interview history found for this session." output_text = "**Your Recent Mock Interviews:**\n\n" for idx, record in enumerate(interview_history_state): timestamp = record.get('timestamp', 'Unknown Time') try: dt = datetime.datetime.fromisoformat(timestamp.replace('Z', '+00:00')) formatted_time = dt.strftime("%Y-%m-%d %H:%M:%S") except Exception as e: print(f"Error parsing timestamp {timestamp}: {e}") formatted_time = timestamp roles = ", ".join(record.get('selected_roles', ['N/A'])) avg_rating = record.get('average_rating', 'N/A') output_text += f"--- **Interview #{len(interview_history_state) - idx} ({formatted_time})** ---\n" output_text += f"**Roles Applied:** {roles}\n" output_text += f"**Average Rating:** {avg_rating:.2f}\n\n" interactions = record.get('interactions', {}) if interactions: output_text += "**Interview Snippets:**\n" count = 0 for q_key, a_val in list(interactions.items())[:3]: q_display = q_key.split(':', 1)[1].strip() if ':' in q_key else q_key a_display = a_val.split(':', 1)[1].strip() if ':' in a_val else a_val output_text += f"- **Q:** {q_display[:100]}{'...' if len(q_display) > 100 else ''}\n" output_text += f" **A:** {a_display[:100]}{'...' if len(a_display) > 100 else ''}\n" count += 1 if count >= 3: break if len(interactions) > 3: output_text += f"... (and {len(interactions) - 3} more questions)\n" else: output_text += "**Details:** Not available.\n" output_text += "\n---\n\n" return output_text load_history_btn.click(fn=load_user_history_local, inputs=[interview_history_state], outputs=[history_output]) if __name__ == "__main__": demo.launch(server_name="0.0.0.0")