gemini_handler.py

# gemini_handler.py
import google.generativeai as genai
import json
import time
import numpy as np
import streamlit as st

# --- Gemini API Setup ---
try:
    GEMINI_API_KEY = st.secrets["GEMINI_API_KEY"]
except:
    GEMINI_API_KEY = "AIzaSyCwEhM7qjRMEEIIpH79A_DlazXa5RozCSM" # Replace with your key

genai.configure(api_key=GEMINI_API_KEY)
GEMINI_MODEL = 'gemini-1.5-flash'

# --- SAFEGUARDS (Improved) ---
FALLBACK_PERSONAL_SWOT = {
    "Strengths": ["Unique perspective and background."],
    "Weaknesses": ["Consider areas for skill development."],
    "Opportunities": ["Leverage personal interests in emerging fields."],
    "Threats": ["Stay aware of the competitive landscape."]
}
FALLBACK_CAREER_SWOT = {
    "Strengths": ["AI analysis could not be completed at this time due to high server load. Please try again later."],
    "Weaknesses": ["Please check your internet connection and API key status."],
    "Opportunities": ["-"],
    "Threats": ["-"]
}

# --- MATHEMATICAL CORRELATION ---
def recommend_careers_correlation(user_riasec, career_profiles):
    recommendations = []
    riasec_order = ["Realistic", "Investigative", "Artistic", "Social", "Enterprising", "Conventional"]
    user_scores = np.array([user_riasec.get(key, 0) for key in riasec_order])

    if np.std(user_scores) < 1e-6:
        sorted_clusters = sorted(list(career_profiles.keys()))
        return [(cluster, 0.0) for cluster in sorted_clusters]

    for career, profile in career_profiles.items():
        career_scores = np.array([profile.get(key, 0) for key in riasec_order])
        correlation_matrix = np.corrcoef(user_scores, career_scores)
        correlation = correlation_matrix[0, 1]
        
        if not np.isnan(correlation):
            recommendations.append((career, correlation))
    
    recommendations.sort(key=lambda x: x[1], reverse=True)
    return recommendations

# --- GEMINI API CALLS ---
def get_personal_swot_analysis(user_data):
    system_instruction = "You are an insightful personal development coach. Analyze the user's profile. Your task is to create a personal SWOT analysis (Strengths, Weaknesses, Opportunities, Threats). Your response MUST be a valid JSON object. Each key must have a value that is a list of 2-3 concise, insightful bullet points (strings)."
    prompt = f"User Profile:\n{json.dumps(user_data, indent=2)}"
    try:
        model = genai.GenerativeModel(GEMINI_MODEL, system_instruction=system_instruction)
        response = model.generate_content(prompt)
        time.sleep(1) # Minimal delay is fine for single calls
        cleaned_response = response.text.strip().replace("```json", "").replace("```", "").strip()
        return json.loads(cleaned_response)
    except Exception as e:
        print(f"Error generating personal SWOT: {e}")
        return FALLBACK_PERSONAL_SWOT

def get_ai_match_scores(user_data, cluster_list):
    system_instruction = "You are an expert career counselor AI. Given a user's detailed profile and a list of 10 career clusters, your task is to provide a final, holistic 'percentage match score' (an integer from 50 to 100) for each. This score MUST consider EVERYTHING in the user's profile. Your response MUST be ONLY a valid JSON object where keys are the career cluster names and values are the integer scores. DO NOT add any other text or formatting."
    prompt = f"User Profile:\n{json.dumps(user_data, indent=2)}\n\nCareer Cluster List to Score:\n{json.dumps(cluster_list)}"
    try:
        model = genai.GenerativeModel(GEMINI_MODEL, system_instruction=system_instruction)
        response = model.generate_content(prompt)
        time.sleep(1)
        cleaned_response = response.text.strip()
        scores = json.loads(cleaned_response)
        return {k: int(v) for k, v in scores.items()}
    except Exception as e:
        print(f"Error generating AI scores, returning random scores: {e}")
        return {cluster: np.random.randint(65, 95) for cluster in cluster_list}

def get_career_swot_analysis_batch(user_data, cluster_list):
    """
    NEW EFFICIENT FUNCTION: Gets SWOT for a list of careers in a single API call.
    """
    system_instruction = f"""
    You are a career strategist AI. For the provided user profile, you must generate a SWOT-style analysis for EACH of the following career clusters: {', '.join(cluster_list)}.
    Your response MUST be a single, valid JSON object. 
    The keys of this object will be the exact career cluster names.
    The value for each key will be another JSON object with four keys: "Strengths", "Weaknesses", "Opportunities", and "Threats".
    The value for each of these SWOT keys must be a list of concise bullet points (strings).
    NEVER fail to produce a valid JSON response. If information is lacking, provide generalized but helpful advice for that point.
    
    Example structure for your response:
    {{
      "IT & Computer Science": {{
        "Strengths": ["Point 1", "Point 2"],
        "Weaknesses": ["Point 1"],
        "Opportunities": ["Point 1", "Point 2"],
        "Threats": ["Point 1"]
      }},
      "Medical Sciences": {{
        "Strengths": ["Point 1"],
        "Weaknesses": ["Point 1", "Point 2"],
        "Opportunities": ["Point 1"],
        "Threats": ["Point 1", "Point 2"]
      }}
    }}
    """
    prompt = f"User Profile:\n{json.dumps(user_data, indent=2)}"
    try:
        model = genai.GenerativeModel(GEMINI_MODEL, system_instruction=system_instruction)
        response = model.generate_content(prompt)
        time.sleep(1)
        cleaned_response = response.text.strip().replace("```json", "").replace("```", "").strip()
        return json.loads(cleaned_response)
    except Exception as e:
        print(f"Error generating batch career SWOT: {e}")
        # Return a fallback for each requested cluster
        return {cluster: FALLBACK_CAREER_SWOT for cluster in cluster_list}