import streamlit as st import json import google.generativeai as genai # Placeholder for your API key - securely manage this in your actual application API_KEY = "AIzaSyCA4__JMC_ZIQ9xQegIj5LOMLhSSrn3pMw" def fetch_data_from_json(filename): """Utility function to fetch data from a given JSON file.""" try: with open(filename, 'r') as file: return json.load(file) except FileNotFoundError: st.error(f"File {filename} not found. Please ensure it's in the correct path.") return None def app(): st.title('Career Insights and Recommendations') # Paths to JSON files - adjust these paths as necessary json_files = { "core_values": "core_values_responses.json", "strengths": "strength_responses.json", "dream_job": "dream_job_info.json", "strengths2": "dynamic_strength_responses.json", "preferences": "preferences_sets.json", "skills_experience": "skills_and_experience_sets.json", "career_priorities": "career_priorities_data.json", } json_files["strengths"] = "strength_responses.json" merge_json_files("strength_responses.json", "dynamic_strength_responses.json", "strength_responses.json") comprehensive_data = {} for key, file_path in json_files.items(): comprehensive_data[key] = fetch_data_from_json(file_path) # Generate and display a comprehensive analysis based on all aspects comprehensive_prompt = construct_comprehensive_prompt(comprehensive_data) st.subheader("Comprehensive Career Analysis") comprehensive_response_text = call_gemini(comprehensive_prompt) st.text("Comprehensive API Response:") st.write(comprehensive_response_text) # Save the comprehensive response save_responses("comprehensive_analysis", comprehensive_response_text) def merge_json_files(file1, file2, output_file): """Merge the contents of two JSON files and save the result in another file.""" try: with open(file1, 'r') as file: data1 = json.load(file) with open(file2, 'r') as file: data2 = json.load(file) # Ensure data1 and data2 are dictionaries if not isinstance(data1, dict): data1 = {} if not isinstance(data2, dict): data2 = {} merged_data = {**data1, **data2} with open(output_file, 'w') as file: json.dump(merged_data, file, indent=4) st.success(f"Merged data saved to {output_file}.") except FileNotFoundError: st.error("One or more input files not found. Please ensure they are in the correct path.") def process_section(section_name, data): """ Processes each section individually by constructing a tailored prompt, calling the Gemini API, and displaying the response. """ prompt = construct_prompt(section_name, data) st.subheader(f"{section_name.replace('_', ' ').title()} Analysis") response_text = call_gemini(prompt) st.text(f"{section_name.replace('_', ' ').title()} API Response:") st.write(response_text) # Save the response save_responses(section_name, response_text) def save_responses(section_name, response_text): """Saves the API responses to a JSON file.""" try: # Attempt to load existing data with open('gemini_responses.json', 'r') as file: responses = json.load(file) except (FileNotFoundError, json.JSONDecodeError): # If the file does not exist or contains invalid data, start with an empty dictionary responses = {} # Update the dictionary with the new response responses[section_name] = response_text # Save the updated dictionary back to the file with open('gemini_responses.json', 'w') as file: json.dump(responses, file, indent=4) def construct_prompt(section_name, data): """ Constructs a detailed and tailored prompt for a specific section, guiding the model to provide insights and recommendations based on that section's data. """ prompt_template = { "career_priorities": "Analyze and evaluate user's current skill level related to these career priorities: {details}.", "core_values": "Assess how user's current behaviours and skills align with these core values: {details}.", "strengths": "Evaluate and highlight user's competency levels across these strengths: {details}.", "dream_job": "Compare user's current skills and experience to the requirements of this dream job: {details}.", "strengths2": "Summarize how user's friend's/collegs/seniors view user's capabilities based on this feedback: {details}.", "preferences": "Judge how well user's skills and attributes fit these preferences: {details}.", "skills_experience": "Assess user's current skill level within this area of expertise: {details}.", } # Constructing the tailored prompt details = json.dumps(data, ensure_ascii=False) prompt = prompt_template.get(section_name, "Please provide data for analysis.").format(details=details) return prompt def construct_comprehensive_prompt(data): prompt_parts = [ "Given an individual's career aspirations, core values, strengths, preferences, and skills, provide a comprehensive analysis that identifies key strengths, aligns these with career values, and suggests career paths. Then, recommend the top 5 job descriptions that would be a perfect fit based on the analysis. Here are the details:", f"Career Priorities: {json.dumps(data['career_priorities'], ensure_ascii=False)}", f"Core Values: {json.dumps(data['core_values'], ensure_ascii=False)}", "Rate the user's career priorities out of 100 and provide justification:", f"Strengths: {json.dumps(data['strengths'], ensure_ascii=False)}", "Rate the user's strengths out of 100 and provide justification:", f"Dream Job Information: {json.dumps(data['dream_job'], ensure_ascii=False)}", "Rate the user's dream job alignment out of 100 and provide justification:", f"Preferences: {json.dumps(data['preferences'], ensure_ascii=False)}", "Rate the user's preferences out of 100 and provide justification:", f"Skills and Experience: {json.dumps(data['skills_experience'], ensure_ascii=False)}", "Rate the user's skills and experience out of 100 and provide justification:", "Based on the analysis, suggest 2-3 areas for mindful upskilling and professional development for the user, along with relevant certifications that would help strengthen their profile:", "Consider the following in the further analysis:", "- Given the strengths and dream job aspirations, what are the top industries or roles that would be a perfect fit?", "- Based on the preferences, what work environment or company culture would be most suitable?", "Conclude with recommendations for the top 5 open job descriptions in India aligned to the user's goals, including any specific industries or companies where these roles may be in demand currently.", ] prompt = "\n\n".join(prompt_parts) return prompt def call_gemini(prompt): """Calls the Gemini API with the given prompt and returns the response.""" # Configure the API with your key genai.configure(api_key=API_KEY) # Set up the model configuration generation_config = { "temperature": 0.7, "top_p": 0.95, "max_output_tokens": 4096, } safety_settings = [ {"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"}, {"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_MEDIUM_AND_ABOVE"}, {"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"}, {"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"}, ] # Create the model instance model = genai.GenerativeModel(model_name="gemini-1.0-pro", generation_config=generation_config, safety_settings=safety_settings) # Generate content response = model.generate_content([prompt]) response_text = response.text return response_text if __name__ == "__main__": app()