radub23
commited on
Commit
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f438e63
1
Parent(s):
1384d3c
Add retry logic and robust tensor handling for intermittent failures
Browse files
app.py
CHANGED
@@ -4,6 +4,7 @@ from fastai.learner import load_learner
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from pathlib import Path
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import pandas as pd
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import os
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"""
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Warning Lamp Detector using FastAI
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@@ -44,31 +45,67 @@ def detect_warning_lamp(image, history: list[tuple[str, str]], system_message):
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if image is None:
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history.append((None, "Please upload an image first."))
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return history
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confidence = float(probs[pred_idx]) # Convert to float for better formatting
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response = f"Detected Warning Lamp: {pred_class}\nConfidence: {confidence:.2%}"
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# Add probabilities for all classes
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response += "\n\nProbabilities for all classes:"
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for i, (cls, prob) in enumerate(zip(learn_inf.dls.vocab, probs)):
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response += f"\n- {cls}: {float(prob):.2%}"
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# Create a custom interface with image upload
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with gr.Blocks(title="Warning Lamp Detector", theme=gr.themes.Soft()) as demo:
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from pathlib import Path
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import pandas as pd
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import os
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import time
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"""
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Warning Lamp Detector using FastAI
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if image is None:
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history.append((None, "Please upload an image first."))
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return history
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# Maximum number of retries
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max_retries = 3
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retry_count = 0
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while retry_count < max_retries:
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try:
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# Convert PIL image to FastAI compatible format
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img = PILImage(image)
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# Get model prediction
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pred_class, pred_idx, probs = learn_inf.predict(img)
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# Try different approaches to handle tensor conversion
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try:
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# First approach - direct conversion
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confidence = float(probs[pred_idx])
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except Exception as e1:
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print(f"First conversion approach failed: {e1}")
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try:
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# Second approach - convert index first
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idx = int(pred_idx)
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confidence = float(probs[idx])
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except Exception as e2:
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print(f"Second conversion approach failed: {e2}")
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# Third approach - use item() method if available
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if hasattr(probs[pred_idx], 'item'):
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confidence = probs[pred_idx].item()
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else:
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# Last resort - use the max probability
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confidence = float(max(probs))
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# Format the prediction results
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response = f"Detected Warning Lamp: {pred_class}\nConfidence: {confidence:.2%}"
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# Add probabilities for all classes
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response += "\n\nProbabilities for all classes:"
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for i, (cls, prob) in enumerate(zip(learn_inf.dls.vocab, probs)):
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try:
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prob_value = float(prob)
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response += f"\n- {cls}: {prob_value:.2%}"
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except Exception as prob_error:
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print(f"Error converting probability for {cls}: {prob_error}")
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response += f"\n- {cls}: N/A"
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# Update chat history
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history.append((None, response))
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return history
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except Exception as e:
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retry_count += 1
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print(f"Attempt {retry_count} failed with error: {e}")
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if retry_count < max_retries:
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print(f"Retrying in 1 second...")
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time.sleep(1) # Wait a bit before retrying
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else:
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error_msg = f"Error processing image after {max_retries} attempts: {str(e)}"
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print(f"All retries failed: {error_msg}")
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history.append((None, error_msg))
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return history
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# Create a custom interface with image upload
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with gr.Blocks(title="Warning Lamp Detector", theme=gr.themes.Soft()) as demo:
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