Delete app.py

app.py

@@ -1,85 +0,0 @@
-import gradio as gr
-from PIL import Image, ImageDraw, ImageFont
-import scipy.io.wavfile as wavfile
-from transformers import pipeline
-
-# Load pipelines
-narrator = pipeline("text-to-speech", model="kakao-enterprise/vits-ljs")
-object_detector = pipeline("object-detection", model="facebook/detr-resnet-50")
-
-# Function to generate audio from text
-def generate_audio(text):
-    narrated_text = narrator(text)
-    wavfile.write("output.wav", rate=narrated_text["sampling_rate"], data=narrated_text["audio"][0])
-    return "output.wav"
-
-# Function to read and summarize detected objects
-def read_objects(detection_objects):
-    object_counts = {}
-    for detection in detection_objects:
-        label = detection['label']
-        object_counts[label] = object_counts.get(label, 0) + 1
-
-    response = "This picture contains"
-    labels = list(object_counts.keys())
-    for i, label in enumerate(labels):
-        response += f" {object_counts[label]} {label}"
-        if object_counts[label] > 1:
-            response += "s"
-        if i < len(labels) - 2:
-            response += ","
-        elif i == len(labels) - 2:
-            response += " and"
-    response += "."
-    return response
-
-# Function to draw bounding boxes on the image
-def draw_bounding_boxes(image, detections):
-    draw_image = image.copy()
-    draw = ImageDraw.Draw(draw_image)
-    font = 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="red", 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="red")
-        draw.text((xmin, ymin), text, fill="white", font=font)
-
-    return draw_image
-
-# Main function to process the image
-def detect_object(image):
-    detections = object_detector(image)
-    processed_image = draw_bounding_boxes(image, detections)
-    description_text = read_objects(detections)
-    processed_audio = generate_audio(description_text)
-    return processed_image, processed_audio
-
-# Gradio interface
-description_text = """
-# Multi-Object Detection with Audio Narration
-
-Upload an image to detect objects and hear a natural language description.
-
-### Credits:
-Developed by Taizun S
-"""
-
-demo = gr.Interface(
-    fn=detect_object,
-    inputs=gr.Image(label="Upload an Image", type="pil"),
-    outputs=[
-        gr.Image(label="Processed Image", type="pil"),
-        gr.Audio(label="Generated Audio")
-    ],
-    title="Multi-Object Detection and Narration",
-    description=description_text,
-)
-
-demo.launch()