Spaces:
Runtime error
Runtime error
Update app.py
Browse files
app.py
CHANGED
@@ -29,6 +29,7 @@ import os
|
|
29 |
from flask import Flask, jsonify, request
|
30 |
import requests
|
31 |
from fetch_data import fetch_and_update_training_data
|
|
|
32 |
|
33 |
# Load configuration file
|
34 |
with open('config.json', 'r') as config_file:
|
@@ -308,3 +309,56 @@ else:
|
|
308 |
model = AutoModelForSequenceClassification.from_pretrained(model_save_path).to('cpu')
|
309 |
tokenizer = AutoTokenizer.from_pretrained(tokenizer_save_path)
|
310 |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
29 |
from flask import Flask, jsonify, request
|
30 |
import requests
|
31 |
from fetch_data import fetch_and_update_training_data
|
32 |
+
import gradio as gr
|
33 |
|
34 |
# Load configuration file
|
35 |
with open('config.json', 'r') as config_file:
|
|
|
309 |
model = AutoModelForSequenceClassification.from_pretrained(model_save_path).to('cpu')
|
310 |
tokenizer = AutoTokenizer.from_pretrained(tokenizer_save_path)
|
311 |
|
312 |
+
#Function to classify user input
|
313 |
+
def classify_user_input(user_input):
|
314 |
+
while True:
|
315 |
+
|
316 |
+
# Tokenize and predict
|
317 |
+
input_encoding = tokenizer(user_input, padding=True, truncation=True, return_tensors="pt").to('cuda')
|
318 |
+
|
319 |
+
with torch.no_grad():
|
320 |
+
#attention_mask = input_encoding['attention_mask'].clone()
|
321 |
+
|
322 |
+
# Modify the attention mask to emphasize certain key tokens
|
323 |
+
for idx, token_id in enumerate(input_encoding['input_ids'][0]):
|
324 |
+
word = tokenizer.decode([token_id])
|
325 |
+
print(word)
|
326 |
+
#if word.strip() in ["point", "summarize", "oil", "maintenance"]: # Target key tokens
|
327 |
+
#attention_mask[0, idx] = 2 # Increase attention weight for these words
|
328 |
+
# else:
|
329 |
+
# attention_mask[0, idx] = 0
|
330 |
+
#print (attention_mask)
|
331 |
+
#input_encoding['attention_mask'] = attention_mask
|
332 |
+
output = model(**input_encoding, output_hidden_states=True)
|
333 |
+
# print('start-logits')
|
334 |
+
# print(output.logits)
|
335 |
+
# print('end-logits')
|
336 |
+
#print(output)
|
337 |
+
attention = output.attentions # Get attention scores
|
338 |
+
#print('atten')
|
339 |
+
#print(attention)
|
340 |
+
# Apply softmax to get the probabilities (confidence scores)
|
341 |
+
probabilities = F.softmax(output.logits, dim=-1)
|
342 |
+
|
343 |
+
# tokens = tokenizer.convert_ids_to_tokens(input_encoding['input_ids'][0].cpu().numpy())
|
344 |
+
# # Display the attention visualization
|
345 |
+
# input_text = tokenizer.convert_ids_to_tokens(input_encoding['input_ids'][0])
|
346 |
+
|
347 |
+
prediction = torch.argmax(output.logits, dim=1).cpu().numpy()
|
348 |
+
|
349 |
+
# Map prediction back to label
|
350 |
+
print(prediction)
|
351 |
+
predicted_label = label_mapping_reverse[prediction[0]]
|
352 |
+
|
353 |
+
|
354 |
+
print(f"Predicted intent: {predicted_label}\n")
|
355 |
+
# Print the confidence for each label
|
356 |
+
print("\nLabel Confidence Scores:")
|
357 |
+
for i, label in label_mapping_reverse.items():
|
358 |
+
confidence = probabilities[0][i].item() # Get confidence score for each label
|
359 |
+
print(f"{label}: {confidence:.4f}")
|
360 |
+
print("\n")
|
361 |
+
|
362 |
+
|
363 |
+
iface = gr.Interface(fn=classify_user_input, inputs="text", outputs="text")
|
364 |
+
iface.launch(share=True)
|