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import streamlit as st
import google.generativeai as genai
import os
# Define a function to check if all sections are filled
def check_all_responses_filled(responses):
return all(responses) and all([all(responses[respondent]) for respondent in respondents])
# Configure the Google Generative AI API if the API key is available
def configure_genai_api():
api_key = os.getenv("GENAI_API_KEY")
if api_key is None:
st.error("API key not found. Please set the GENAI_API_KEY environment variable.")
return None
else:
genai.configure(api_key=api_key)
generation_config = {
"temperature": 0.9,
"top_p": 1,
"top_k": 40,
"max_output_tokens": 2048,
}
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"},
]
return genai.GenerativeModel(model_name="gemini-1.0-pro",
generation_config=generation_config,
safety_settings=safety_settings)
# Function to combine responses from all sections
def combine_responses(saved_data):
def extract_strings(data):
if isinstance(data, dict):
return " ".join([extract_strings(value) for value in data.values()])
elif isinstance(data, list):
return " ".join([extract_strings(item) for item in data])
elif isinstance(data, str):
return data
else:
return ""
combined_responses = []
for section, responses in saved_data.items():
for response in responses.values():
combined_responses.append(extract_strings(response))
return " ".join(combined_responses)
def app():
st.header("Summary of Your Professional Profile")
# Check for environment variable and configure the API
model = configure_genai_api()
if not model:
return
# Display and collect all responses
all_responses = {
'core_values': st.session_state.get('core_values_responses'),
'strengths': st.session_state.get('strength_responses'),
'partner_assessment': st.session_state.get('stories_feedback'),
'network_feedback': st.session_state.get('network_feedback'),
'skills': st.session_state.get('core_skills_responses'),
'priorities': st.session_state.get('priorities_data_saved'),
'preferences': st.session_state.get('preferences_responses'),
}
# Display all collected responses
for section, responses in all_responses.items():
if responses:
st.subheader(f"Your {section.replace('_', ' ').title()}:")
if isinstance(responses, dict):
for question, response in responses.items():
st.text(f"Q: {question}")
st.text(f"A: {response}")
elif isinstance(responses, list):
for response in responses:
st.text(response)
st.write("---") # Separator line
# Check if all sections are completed
all_responses_filled = all(responses is not None for responses in all_responses.values())
if st.button("Generate My Career Summary") and all_responses_filled:
combined_responses_text = combine_responses(all_responses)
inputs = """
Based on the following inputs, provide recommendations for job opportunities, core values, strengths, and weaknesses, as well as suggested certifications to help the individual align with their desired career path.
Inputs:
"""
prompt_template = """
Next Job:
1. List the best suitable job role based on the user's preferences and strengths.
3. Provide a brief explanation of how the job aligns with the user's strengths and values.
Core Values:
1. List the core values user has
2. Calculate the rating of core values the user possesses based on the recommended job profile. Give the rating in percentage.
3. Identify any core values the user should develop to better align with the recommended job profile.
4. Status: user_has if user has it, user_should_develop, if user should develop it
Strengths:
1. List the strengths user has.
2. Calculate the rating of strengths the user possesses based on the recommended job profile. Give the rating in percentage.
3. Identify any strengths the user should develop to better align with the recommended job profile.
4. Status: user_has if user has it, user_should_develop, if user should develop it
Weaknesses:
1. Based on the user's responses, list the weaknesses along with justification.
2. Calculate the rating of weaknesses the user possesses based on the recommended job profile. Give the rating in percentage.
3. Explanation for the same based on the user input.
Career Path Analysis:
Provide a detailed narrative that connects the user's strengths and values with the suggested career paths.
Recommended Certifications:
1. Suggest relevant certifications from Udacity, Udemy, and Coursera platforms that can help the user acquire the necessary skills and knowledge for their desired career path.
2. Provide a brief description of each recommended certification, but do not include links.
"""
prompt = inputs + combined_responses_text + prompt_template
try:
# Attempt to generate a comprehensive career summary and future projection
response = model.generate_content([prompt])
if response and hasattr(response, 'parts'):
career_summary = ''.join([part['text'] for part in response.parts])
st.subheader("Career Summary and Projection")
st.write("Based on all the information you've provided, here's a comprehensive summary and future career projection tailored to you:")
st.info(career_summary)
else:
st.error("The response from the API was not in the expected format.")
except Exception as e:
st.error("An error occurred while generating your career summary. Please try again later.")
st.error(f"Error: {e}")
elif not all_responses_filled:
st.error("Please ensure all sections are completed to receive your comprehensive career summary.")
if __name__ == "__main__":
app() |