Create app.py
Browse files
app.py
ADDED
@@ -0,0 +1,44 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
1 |
+
import gradio as gr
|
2 |
+
from transformers import AutoTokenizer, AutoModelForSequenceClassification
|
3 |
+
|
4 |
+
# Load Pretrained Model and Tokenizer
|
5 |
+
model_name = "text/sentiment-analysis"
|
6 |
+
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
7 |
+
model = AutoModelForSequenceClassification.from_pretrained(model_name)
|
8 |
+
|
9 |
+
# Define the Gradio Interface
|
10 |
+
import easyocr
|
11 |
+
def image_classifier(img):
|
12 |
+
model = YOLO('/content/drive/MyDrive/SIH_2023/YOLO/runs/detect/train/weights/best.pt')
|
13 |
+
res = model.predict(img,conf=0.25)
|
14 |
+
box = res[0].boxes.xywh[0]
|
15 |
+
bounding_box = box.cpu().numpy()
|
16 |
+
x0 = bounding_box[0] - bounding_box[2] / 2
|
17 |
+
x1 = bounding_box[0] + bounding_box[2] / 2
|
18 |
+
y0 = bounding_box[1] - bounding_box[3] / 2
|
19 |
+
y1 = bounding_box[1] + bounding_box[3] / 2
|
20 |
+
|
21 |
+
start_point = (int(x0), int(y0))
|
22 |
+
end_point = (int(x1), int(y1))
|
23 |
+
cv2.rectangle(img, start_point, end_point, color=(0,255,0), thickness=2)
|
24 |
+
# Use the easyocr reader for English language
|
25 |
+
reader = easyocr.Reader(['en'])
|
26 |
+
|
27 |
+
# Perform OCR on the input image
|
28 |
+
result = reader.readtext(img,allowlist="0123456789")
|
29 |
+
|
30 |
+
# Extract text and bounding box coordinates
|
31 |
+
text_and_coordinates = [(entry[1], entry[0]) for entry in result]
|
32 |
+
return text_and_coordinates
|
33 |
+
|
34 |
+
iface = gr.Interface(
|
35 |
+
fn=predict_sentiment,
|
36 |
+
inputs="text",
|
37 |
+
outputs="text",
|
38 |
+
live=True,
|
39 |
+
title="Sentiment Analysis",
|
40 |
+
description="Enter a sentence to predict sentiment.",
|
41 |
+
)
|
42 |
+
|
43 |
+
# Launch the Gradio Interface
|
44 |
+
iface.launch()
|