analyze.py

import streamlit as st
import json
import google.generativeai as genai

def app():

    # Configure Google Generative AI
    genai.configure(api_key="AIzaSyD-61G3GhSY97O-X2AlpXGv1MYBBMRFmwg")

    # Streamlit UI
    st.title("Analysis of Responses")

    uploaded_file = st.file_uploader("Choose a JSON file", type="json")
    if uploaded_file is not None:
        # Load the JSON file
        data = json.load(uploaded_file)
        # Set up the model configuration
        generation_config = {
            "temperature": 0.9,
            "top_p": 1,
            "top_k": 1,
            "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"
            },
        ]

        model = genai.GenerativeModel(model_name="gemini-1.0-pro",
                                    generation_config=generation_config,
                                    safety_settings=safety_settings)

        # UI to trigger processing
        if st.button("Generate Content"):
            # Adjust the following line to match how you extract prompts from your JSON data
            prompt_parts = f"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: {data}"
            response = model.generate_content(prompt_parts)
            
            # Display the response
            st.write("Generated Content:")
            st.write(response.text)