Create app.py

app.py

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+from transformers import pipeline
+from transformers.utils import logging
+from helper import ignore_warnings
+
+logging.set_verbosity_error()
+ignore_warnings()
+
+od_pipe = pipeline("object-detection", model="facebook/detr-resnet-50")
+tts_pipe = pipeline("text-to-speech", model="kakao-enterprise/vits-ljs")
+
+def detect_objects(pil_image):
+    pipeline_output = od_pipe(pil_image)
+    processed_image = render_results_in_image(pil_image, pipeline_output)
+    text = summarize_predictions_natural_language(pipeline_output)
+    narrated_text = tts_pipe(text)
+    sr=narrated_text["sampling_rate"]
+    audio = narrated_text["audio"][0]
+    
+    return processed_image, text,(sr, audio)
+
+demo = gr.Interface(
+  fn=detect_objects,
+  inputs=gr.Image(label="Input image",
+                  type="pil"),
+  outputs=[gr.Image(label="Output image with predicted instances",
+                   type="pil"), gr.Textbox(label='Description of detected objects', lines=3),
+            gr.Audio(label='Play audio to listen about the detected objectes in the image')]
+
+  # outputs=["audio"]
+)
+
+demo.launch(debug=True)