File size: 1,962 Bytes
5637ddb 9919c9b 5637ddb f1cb329 5637ddb | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 | import numpy as np
import warnings
from common.rag.common import generate_personality_summary
import os
os.environ["TF_USE_LEGACY_KERAS"] = "1" # Add this line here
import tensorflow as tf
_model = None
def predict_handwriting(image):
"""
Preprocess uploaded image exactly the way model expects
"""
global _model
if _model is None:
_model = tf.keras.models.load_model(
"signature_model_tfdata.h5"
)
if image is None:
return "Please upload an image.", ""
try:
img = tf.keras.preprocessing.image.img_to_array(image)
if img.shape[-1] == 4:
img = img[..., :3]
elif img.shape[-1] == 1:
pass
elif img.shape[-1] != 3:
return "Unsupported image format (channels).", ""
if img.shape[-1] == 3:
img = tf.image.rgb_to_grayscale(img)
IMG_SIZE = 224
img = tf.image.resize(img, [IMG_SIZE, IMG_SIZE])
img = img / 255.0
img = tf.image.grayscale_to_rgb(img)
img = tf.expand_dims(img, axis=0)
predictions = _model.predict(img, verbose=0)[0]
predicted_idx = np.argmax(predictions)
confidence = float(predictions[predicted_idx]) * 100
CLASS_NAMES = [
"Agreeableness",
"Conscientiousness",
"Extraversion",
"Neuroticism",
"Openness"
]
trait = CLASS_NAMES[predicted_idx]
result = f"**Predicted Personality Trait**\n{trait}\n\n**Confidence**: {confidence:.2f}%", trait
return result
except Exception as e:
import traceback
return f"Error during prediction:\n{str(e)}", ""
def full_analysis(image):
if image is None:
return "Please upload an image.", ""
prediction_text, trait = predict_handwriting(image)
summary = generate_personality_summary(trait) if trait else ""
return prediction_text, summary |