Spaces:
Sleeping
Sleeping
Update app.py
Browse files
app.py
CHANGED
@@ -1,38 +1,30 @@
|
|
1 |
-
import gradio as gr
|
2 |
from PIL import Image, ImageDraw, ImageFont
|
3 |
import scipy.io.wavfile as wavfile
|
4 |
import numpy as np
|
5 |
-
|
6 |
from transformers import pipeline
|
7 |
from collections import Counter
|
8 |
import inflect
|
9 |
|
10 |
-
#
|
11 |
-
# tts_model_path = ("../Models/models--kakao-enterprise--vits-ljs/snapshots/"
|
12 |
-
# "3bcb8321394f671bd948ebf0d086d694dda95464")
|
13 |
-
# narrator = pipeline("text-to-speech", model=tts_model_path)
|
14 |
-
|
15 |
narrator = pipeline("text-to-speech", model="kakao-enterprise/vits-ljs")
|
16 |
-
|
17 |
-
# obj_detector_path = ("../Models/models--facebook--detr-resnet-50/snapshots/"
|
18 |
-
# "1d5f47bd3bdd2c4bbfa585418ffe6da5028b4c0b")
|
19 |
-
# obj_detector = pipeline("object-detection", model=obj_detector_path)
|
20 |
-
|
21 |
obj_detector = pipeline("object-detection", model="facebook/detr-resnet-50")
|
22 |
|
23 |
-
|
|
|
24 |
narrated = narrator(text)
|
25 |
-
|
26 |
audio = narrated["audio"]
|
27 |
sampling_rate = narrated["sampling_rate"]
|
28 |
|
29 |
-
# Convert to int16 if needed
|
30 |
if audio.dtype != np.int16:
|
31 |
audio = (audio * 32767).astype(np.int16)
|
32 |
|
33 |
-
|
34 |
-
|
|
|
35 |
|
|
|
36 |
def read_objects(detections: list[dict]) -> str:
|
37 |
if not detections:
|
38 |
return "No objects were detected in this picture."
|
@@ -53,6 +45,7 @@ def read_objects(detections: list[dict]) -> str:
|
|
53 |
|
54 |
return f"This picture contains {result}."
|
55 |
|
|
|
56 |
def draw_detected_objects(image, detections, score_threshold=0.5):
|
57 |
annotated_image = image.copy()
|
58 |
draw = ImageDraw.Draw(annotated_image)
|
@@ -92,21 +85,24 @@ def draw_detected_objects(image, detections, score_threshold=0.5):
|
|
92 |
|
93 |
return annotated_image
|
94 |
|
|
|
95 |
def detect_image(image):
|
96 |
-
|
97 |
-
|
98 |
-
|
99 |
-
|
100 |
-
|
101 |
-
|
102 |
-
|
|
|
|
|
|
|
|
|
|
|
103 |
gr.close_all()
|
104 |
|
105 |
-
demo = gr.Interface(
|
106 |
-
|
107 |
-
|
108 |
-
|
109 |
-
|
110 |
-
demo.launch()
|
111 |
-
|
112 |
-
|
|
|
1 |
+
import gradio as gr
|
2 |
from PIL import Image, ImageDraw, ImageFont
|
3 |
import scipy.io.wavfile as wavfile
|
4 |
import numpy as np
|
5 |
+
import tempfile
|
6 |
from transformers import pipeline
|
7 |
from collections import Counter
|
8 |
import inflect
|
9 |
|
10 |
+
# Load models
|
|
|
|
|
|
|
|
|
11 |
narrator = pipeline("text-to-speech", model="kakao-enterprise/vits-ljs")
|
|
|
|
|
|
|
|
|
|
|
12 |
obj_detector = pipeline("object-detection", model="facebook/detr-resnet-50")
|
13 |
|
14 |
+
# Generate audio and save as temporary .wav
|
15 |
+
def generate_audio(text):
|
16 |
narrated = narrator(text)
|
|
|
17 |
audio = narrated["audio"]
|
18 |
sampling_rate = narrated["sampling_rate"]
|
19 |
|
|
|
20 |
if audio.dtype != np.int16:
|
21 |
audio = (audio * 32767).astype(np.int16)
|
22 |
|
23 |
+
with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as f:
|
24 |
+
wavfile.write(f.name, int(sampling_rate), audio)
|
25 |
+
return f.name
|
26 |
|
27 |
+
# Turn detections into human-friendly text
|
28 |
def read_objects(detections: list[dict]) -> str:
|
29 |
if not detections:
|
30 |
return "No objects were detected in this picture."
|
|
|
45 |
|
46 |
return f"This picture contains {result}."
|
47 |
|
48 |
+
# Annotate the image with bounding boxes and labels
|
49 |
def draw_detected_objects(image, detections, score_threshold=0.5):
|
50 |
annotated_image = image.copy()
|
51 |
draw = ImageDraw.Draw(annotated_image)
|
|
|
85 |
|
86 |
return annotated_image
|
87 |
|
88 |
+
# Gradio function
|
89 |
def detect_image(image):
|
90 |
+
try:
|
91 |
+
raw_image = image
|
92 |
+
output = obj_detector(raw_image)
|
93 |
+
processed_image = draw_detected_objects(raw_image, output)
|
94 |
+
natural_text = read_objects(output)
|
95 |
+
processed_audio = generate_audio(natural_text)
|
96 |
+
return processed_image, processed_audio
|
97 |
+
except Exception as e:
|
98 |
+
print("❌ Error:", e)
|
99 |
+
return None, None
|
100 |
+
|
101 |
+
# Launch Gradio app
|
102 |
gr.close_all()
|
103 |
|
104 |
+
demo = gr.Interface(
|
105 |
+
fn=detect_image,
|
106 |
+
inputs=[gr.Image(label="Upload an Image", type="pil")],
|
107 |
+
outputs=[
|
108 |
+
gr.Image(label="Image with Detecte
|
|
|
|
|
|