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import tensorflow as tf
from pydantic import BaseModel
from typing import List
from fastapi import FastAPI
import numpy as np
import requests
from fastapi.middleware.cors import CORSMiddleware
import pandas as pd
from sklearn.preprocessing import MinMaxScaler
from sklearn.cluster import KMeans
from keras.models import load_model
model = load_model('models/big5_gru.h5')
class PersonalityRequest(BaseModel):
responses: List[float]
class PersonalityResponse(BaseModel):
personality_type: int
personality_name: str
personality_mapping = {
0: 'Extroverted',
1: 'Neurotic',
2: 'Agreeable',
3: 'Conscientious',
4: 'Open'
}
origins = ["*"]
app = FastAPI()
app.add_middleware(
CORSMiddleware,
allow_origins=origins,
allow_credentials=True,
allow_methods=["*"],
allow_headers=["*"],
)
def five2one(item):
return (item - 0) / (5 - 0)
model_List = ["gpt-4", "gpt-4-0613", "gpt-3.5-turbo-16k-0613", "gpt-3.5-long"]
def get_response(model, prompt):
url = "https://gptapi.lumaticai.com/v1/chat/completions"
body = {
"model": model,
"stream": False,
"messages": [
{"role": "assistant", "content": prompt}
]
}
try:
json_response = requests.post(url, json=body).json().get('choices', [])
print(model)
for choice in json_response:
# print(choice.get('message', {}).get('content', ''))
res = choice.get('message', {}).get('content', '')
return res
except Exception as e:
# print('Error : ', e)
return e
def get_response_with_fallback(prompt):
for model in model_List:
try:
response = get_response(model, prompt)
if not isinstance(response, Exception): # Check if it's an error
return {"response": response, "model": model}
except Exception as e:
return f"Error with model {model}: {e}"
# If none succeed, return an error message
return "Failed to generate diagnosis with any model."
def preprocess_data(new_data_path):
# Load new data
new_data = pd.DataFrame(new_data_path)
# Scale the data using the same scaler used during training
scaler = MinMaxScaler(feature_range=(0, 1))
scaled_data = scaler.fit_transform(new_data)
# Reshape the data to fit the model input
reshaped_data = scaled_data.reshape((scaled_data.shape[1], scaled_data.shape[0], 1))
return reshaped_data
def predict_clusters(model, preprocessed_data):
# Predict the cluster for each instance
predictions = model.predict(preprocessed_data)
# Get the cluster with the highest probability
predicted_clusters = np.argmax(predictions, axis=1)
return predicted_clusters
@app.post("/predict", response_model=PersonalityResponse)
def predict_personality(request: PersonalityRequest):
preprocessed_data = preprocess_data(request.responses)
predicted_clusters = predict_clusters(model, preprocessed_data)
personality_type = predicted_clusters[0]
personality_name = personality_mapping[predicted_clusters[0]]
print(personality_name)
return PersonalityResponse(personality_type=personality_type, personality_name=personality_name)
@app.post('/personality/description')
def personality_description(personality_name : str):
prompt = f"\nYou are an psychology expert. I will give you a personality name based on The Big Five OCEAN Personality types. you will provide me with the description of the personality, tell everything about the personality type and the person who have this type of personality. \nPersonality type: {personality_name}. Answer in this format: \nPersonality description: "
try:
resultt = get_response_with_fallback(prompt)
modell = resultt.get('model')
resultsj = resultt.get('response')
results = str(resultsj)
# rst = resultt.response
if isinstance(results, Exception):
return {"message": "Error generating diagnosis: " + results, "model": modell}
for line in results.split('\n'):
if line.startswith('"**Personality description:'):
ps = line.split(':**')[1].strip()
elif line.startswith('**Personality description:'):
ps = line.split(':**')[1].strip()
elif line.startswith('Personality description:'):
ps = line.split(':')[1].strip()
return ps
except Exception as e:
return {"message": "couldn't get description for " + personality_name}
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="0.0.0.0", port=8000)
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