Update app.py

app.py

@@ -160,10 +160,10 @@ def classify_toxicity(audio_file, text_input, classify_anxiety, emo_class, expli
 def intervention_output(intervene):
             if intervene == "Audio File":
                 print("audio updated")
-                return { output_col : gr.update(visible=True), out_aud : gr.update(value="calm.wav", visible=True, autoplay=True)} 
+                return { output_col : gr.update(visible=True), out_aud : gr.update(value="./calm.wav", visible=True, autoplay=True)} 
             elif intervene == "Therapy App":
                 print("therapy app updated")
-                return { output_col : gr.update(visible=True), out_img : gr.update(value="hrv-breathing.gif", visible=True)}
+                return { output_col : gr.update(visible=True), out_img : gr.update(value="./hrv-breathing.gif", visible=True)}
             elif intervene == "Text Message":
                 phrase = positive_affirmations()
                 return { output_col : gr.update(visible=True), out_text : gr.update(value=phrase, visible=True)}
@@ -197,8 +197,8 @@ with gr.Blocks() as iface:
         out_class = gr.Textbox()
     with gr.Column(visible=False) as output_col:
         out_text = gr.Textbox()
-        out_img = gr.Image(value="hrv-breathing.gif")
-        out_aud = gr.Audio(value="calm.wav")
+        out_img = gr.Image(value="./hrv-breathing.gif")
+        out_aud = gr.Audio(value="./calm.wav")
     submit_btn.click(fn=classify_toxicity, inputs=[aud_input, text, anxiety_class, emo_class, explit_preference, sense_slider, intervention_type], outputs=[out_val, out_class, out_text, output_col])
 
 iface.launch()