Create app.py

app.py

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+import gradio as gr
+from PIL import Image, ImageDraw, ImageFont
+import scipy.io.wavfile as wavfile
+from transformers import pipeline
+
+# Initialize pipelines
+narrator = pipeline("text-to-speech", model="kakao-enterprise/vits-ljs")
+object_detector = pipeline("object-detection", model="facebook/detr-resnet-101")
+
+# Constants
+FONT_PATH = None  # Update this with the path to your custom font if needed
+FONT_SIZE = 20
+BOX_COLOR = "red"
+TEXT_BACKGROUND_COLOR = "red"
+TEXT_COLOR = "white"
+
+
+def generate_audio(text):
+    try:
+        # Generate the narrated text
+        narrated_text = narrator(text)
+        # Save the audio to a WAV file
+        wavfile.write("output.wav", rate=narrated_text["sampling_rate"],
+                      data=narrated_text["audio"][0])
+        return "output.wav"
+    except Exception as e:
+        print(f"Error generating audio: {e}")
+        return None
+
+
+def count_objects(detection_objects):
+    object_counts = {}
+    for detection in detection_objects:
+        label = detection['label']
+        if label in object_counts:
+            object_counts[label] += 1
+        else:
+            object_counts[label] = 1
+    return object_counts
+
+
+def generate_text_from_objects(object_counts):
+    response = "This picture contains"
+    labels = list(object_counts.keys())
+    for i, label in enumerate(labels):
+        count = object_counts[label]
+        response += f" {count} {label}"
+        if count > 1:
+            response += "s"
+        if i < len(labels) - 2:
+            response += ","
+        elif i == len(labels) - 2:
+            response += " and"
+    response += "."
+    return response
+
+
+def draw_bounding_boxes(image, detections, font_path=FONT_PATH, font_size=FONT_SIZE):
+    draw_image = image.copy()
+    draw = ImageDraw.Draw(draw_image)
+    font = ImageFont.truetype(font_path, font_size) if font_path else ImageFont.load_default()
+    
+    for detection in detections:
+        box = detection['box']
+        xmin, ymin, xmax, ymax = box['xmin'], box['ymin'], box['xmax'], box['ymax']
+        draw.rectangle([(xmin, ymin), (xmax, ymax)], outline=BOX_COLOR, width=3)
+        
+        label = detection['label']
+        score = detection['score']
+        text = f"{label} {score:.2f}"
+        
+        text_size = draw.textbbox((xmin, ymin), text, font=font)
+        draw.rectangle([(text_size[0], text_size[1]), (text_size[2], text_size[3])], fill=TEXT_BACKGROUND_COLOR)
+        draw.text((xmin, ymin), text, fill=TEXT_COLOR, font=font)
+    
+    return draw_image
+
+
+def detect_object(image):
+    try:
+        detections = object_detector(image)
+        processed_image = draw_bounding_boxes(image, detections)
+        object_counts = count_objects(detections)
+        natural_text = generate_text_from_objects(object_counts)
+        processed_audio = generate_audio(natural_text)
+        return processed_image, processed_audio
+    except Exception as e:
+        print(f"Error in object detection: {e}")
+        return None, None
+
+
+demo = gr.Interface(
+    fn=detect_object,
+    inputs=[gr.Image(label="Select Image", type="pil")],
+    outputs=[gr.Image(label="Processed Image", type="pil"), gr.Audio(label="Generated Audio")],
+    title="AI-Powered Object Detection with Audio Feedback",
+    description="Upload an image and get object detection results using the DETR model with a ResNet-101 backbone with Audio Feedback"
+)
+
+demo.launch()