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import streamlit as st |
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import json |
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import google.generativeai as genai |
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API_KEY = "AIzaSyCA4__JMC_ZIQ9xQegIj5LOMLhSSrn3pMw" |
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def fetch_data_from_json(filename): |
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"""Utility function to fetch data from a given JSON file.""" |
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try: |
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with open(filename, 'r') as file: |
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return json.load(file) |
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except FileNotFoundError: |
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st.error(f"File {filename} not found. Please ensure it's in the correct path.") |
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return None |
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def app(): |
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st.title('Career Insights and Recommendations') |
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json_files = { |
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"core_values": "core_values_responses.json", |
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"strengths": "strength_responses.json", |
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"dream_job": "dream_job_info.json", |
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"strengths2": "dynamic_strength_responses.json", |
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"preferences": "preferences_sets.json", |
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"skills_experience": "skills_and_experience_sets.json", |
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"career_priorities": "career_priorities_data.json", |
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} |
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json_files["strengths"] = "strength_responses.json" |
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merge_json_files("strength_responses.json", "dynamic_strength_responses.json", "strength_responses.json") |
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comprehensive_data = {} |
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for key, file_path in json_files.items(): |
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comprehensive_data[key] = fetch_data_from_json(file_path) |
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comprehensive_prompt = construct_comprehensive_prompt(comprehensive_data) |
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st.subheader("Comprehensive Career Analysis") |
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comprehensive_response_text = call_gemini(comprehensive_prompt) |
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st.text("Comprehensive API Response:") |
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st.write(comprehensive_response_text) |
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save_responses("comprehensive_analysis", comprehensive_response_text) |
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def merge_json_files(file1, file2, output_file): |
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"""Merge the contents of two JSON files and save the result in another file.""" |
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try: |
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with open(file1, 'r') as file: |
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data1 = json.load(file) |
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with open(file2, 'r') as file: |
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data2 = json.load(file) |
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if not isinstance(data1, dict): |
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data1 = {} |
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if not isinstance(data2, dict): |
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data2 = {} |
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merged_data = {**data1, **data2} |
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with open(output_file, 'w') as file: |
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json.dump(merged_data, file, indent=4) |
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st.success(f"Merged data saved to {output_file}.") |
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except FileNotFoundError: |
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st.error("One or more input files not found. Please ensure they are in the correct path.") |
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def process_section(section_name, data): |
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""" |
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Processes each section individually by constructing a tailored prompt, |
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calling the Gemini API, and displaying the response. |
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""" |
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prompt = construct_prompt(section_name, data) |
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st.subheader(f"{section_name.replace('_', ' ').title()} Analysis") |
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response_text = call_gemini(prompt) |
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st.text(f"{section_name.replace('_', ' ').title()} API Response:") |
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st.write(response_text) |
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save_responses(section_name, response_text) |
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def save_responses(section_name, response_text): |
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"""Saves the API responses to a JSON file.""" |
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try: |
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with open('gemini_responses.json', 'r') as file: |
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responses = json.load(file) |
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except (FileNotFoundError, json.JSONDecodeError): |
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responses = {} |
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responses[section_name] = response_text |
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with open('gemini_responses.json', 'w') as file: |
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json.dump(responses, file, indent=4) |
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def construct_prompt(section_name, data): |
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""" |
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Constructs a detailed and tailored prompt for a specific section, |
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guiding the model to provide insights and recommendations based on that section's data. |
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""" |
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prompt_template = { |
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"career_priorities": "Analyze and evaluate user's current skill level related to these career priorities: {details}.", |
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"core_values": "Assess how user's current behaviours and skills align with these core values: {details}.", |
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"strengths": "Evaluate and highlight user's competency levels across these strengths: {details}.", |
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"dream_job": "Compare user's current skills and experience to the requirements of this dream job: {details}.", |
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"strengths2": "Summarize how user's friend's/collegs/seniors view user's capabilities based on this feedback: {details}.", |
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"preferences": "Judge how well user's skills and attributes fit these preferences: {details}.", |
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"skills_experience": "Assess user's current skill level within this area of expertise: {details}.", |
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} |
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details = json.dumps(data, ensure_ascii=False) |
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prompt = prompt_template.get(section_name, "Please provide data for analysis.").format(details=details) |
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return prompt |
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def construct_comprehensive_prompt(data): |
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prompt_parts = [ |
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"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:", |
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f"Career Priorities: {json.dumps(data['career_priorities'], ensure_ascii=False)}", |
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f"Core Values: {json.dumps(data['core_values'], ensure_ascii=False)}", |
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"Rate the user's career priorities out of 100 and provide justification:", |
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f"Strengths: {json.dumps(data['strengths'], ensure_ascii=False)}", |
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"Rate the user's strengths out of 100 and provide justification:", |
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f"Dream Job Information: {json.dumps(data['dream_job'], ensure_ascii=False)}", |
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"Rate the user's dream job alignment out of 100 and provide justification:", |
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f"Preferences: {json.dumps(data['preferences'], ensure_ascii=False)}", |
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"Rate the user's preferences out of 100 and provide justification:", |
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f"Skills and Experience: {json.dumps(data['skills_experience'], ensure_ascii=False)}", |
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"Rate the user's skills and experience out of 100 and provide justification:", |
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"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:", |
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"Consider the following in the further analysis:", |
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"- Given the strengths and dream job aspirations, what are the top industries or roles that would be a perfect fit?", |
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"- Based on the preferences, what work environment or company culture would be most suitable?", |
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"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.", |
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] |
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prompt = "\n\n".join(prompt_parts) |
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return prompt |
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def call_gemini(prompt): |
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"""Calls the Gemini API with the given prompt and returns the response.""" |
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genai.configure(api_key=API_KEY) |
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generation_config = { |
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"temperature": 0.7, |
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"top_p": 0.95, |
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"max_output_tokens": 4096, |
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} |
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safety_settings = [ |
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{"category": "HARM_CATEGORY_HARASSMENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"}, |
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{"category": "HARM_CATEGORY_HATE_SPEECH", "threshold": "BLOCK_MEDIUM_AND_ABOVE"}, |
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{"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"}, |
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{"category": "HARM_CATEGORY_DANGEROUS_CONTENT", "threshold": "BLOCK_MEDIUM_AND_ABOVE"}, |
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] |
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model = genai.GenerativeModel(model_name="gemini-1.0-pro", |
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generation_config=generation_config, |
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safety_settings=safety_settings) |
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response = model.generate_content([prompt]) |
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response_text = response.text |
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return response_text |
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if __name__ == "__main__": |
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app() |
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