import random
import uuid
import json
from transformers import AutoTokenizer, AutoModelForCausalLM


# Load the tokenizer and model without FlashAttention2
tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3.5-vision-instruct")

# Disable FlashAttention2 by forcing default attention
config = {
    'attn_implementation': 'default'
}

model = AutoModelForCausalLM.from_pretrained(
    "microsoft/Phi-3.5-vision-instruct",
    trust_remote_code=True,
    **config
)




# Function to generate text using the model
def generate_text(prompt):
    inputs = tokenizer(prompt, return_tensors="pt")
    outputs = model.generate(inputs['input_ids'], max_length=150)
    return tokenizer.decode(outputs[0], skip_special_tokens=True)

# Lists for random selection
interest_list = ["networking", "dealMaking"]
personal_interest_list = [
    "Drawing", "Cooking", "Foodie", "Board Gaming", "Card games", "Checkers", "Chess", 
    "PC gaming", "Puzzle solving", "Video gaming", "Reading", "Science experiments", 
    "Magic and illusion", "Stand-up comedy", "Walking", "Beer brewing", "Gourmet food exploration", 
    "Mixology", "Wine tasting"
]
professional_interest_list = [
    "Building a customer-centric culture", "Community Building", "Community Management", 
    "Delivering exceptional customer service", "Ensuring product/service quality", 
    "Handling customer complaints and feedback"
]

# Function to generate random profile with intelligent education/professional details
def generate_intelligent_profile():
    # Randomly select interests and location
    profile = {
        "_id": str(uuid.uuid4()),  # Generate random ID
        "latitude": random.uniform(-90, 90),  # Random latitude
        "longitude": random.uniform(-180, 180),  # Random longitude
        "interest": random.sample(interest_list, random.randint(1, len(interest_list))),  # Random interests
        "personalInterest": random.sample(personal_interest_list, random.randint(3, 7)),  # Random personal interests
        "professionalInterest": random.sample(professional_interest_list, random.randint(3, 5))  # Random professional interests
    }
    
    # Generate intelligent educational details using the model
    education_prompt = "Generate an educational background for a professional profile with relevant qualifications."
    profile["educationalDetails"] = [generate_text(education_prompt)]
    
    # Generate intelligent professional details using the model
    professional_prompt = "Generate a professional career summary for a profile with achievements and roles."
    profile["professionalDetails"] = [generate_text(professional_prompt)]
    
    return profile

# Function to generate multiple profiles
def generate_profiles(num_profiles=5):
    profiles = [generate_intelligent_profile() for _ in range(num_profiles)]
    return {"data": profiles}

# Generate and print random intelligent profiles
generated_profiles = generate_profiles(3)
print(json.dumps(generated_profiles, indent=4))