test

app.py

@@ -6,6 +6,7 @@ import numpy as np
 from transformers import pipeline
 import torch
 import soundfile as sf
+import json  # For JSON response
 
 # Page configuration
 st.set_page_config(page_title="Audio Processing App", layout="wide")
@@ -24,12 +25,12 @@ def load_models():
         
         models = {
             'transcriber': pipeline("automatic-speech-recognition", 
-                                 model="openai/whisper-tiny.en", 
-                                 device=device,
-                                 chunk_length_s=30),  # Process in 30-second chunks
+                                     model="openai/whisper-tiny.en", 
+                                     device=device,
+                                     chunk_length_s=30),  # Process in 30-second chunks
             'summarizer': pipeline("summarization", 
-                                model="sshleifer/distilbart-cnn-12-6", 
-                                device=device)
+                                   model="sshleifer/distilbart-cnn-12-6", 
+                                   device=device)
         }
         return models, None
     except Exception as e:
@@ -148,9 +149,23 @@ def main():
             st.session_state.models_loaded = True
             st.session_state.models = models
     
-    # File uploader with clear instructions
+    # Check if an audio file was uploaded via API
+    query_params = st.experimental_get_query_params()
+    if "file" in query_params:
+        audio_file_path = query_params["file"][0]  # This should be the path to the uploaded audio file
+
+        # Process the audio
+        results = process_audio(audio_file_path, st.session_state.models)
+
+        if results:
+            # Return the results as JSON
+            st.json(results)
+        return  # Exit the function early to avoid further processing in the UI
+
+    # Normal Streamlit UI flow for file upload
     st.write("Upload an audio file of your lecture (supported formats: WAV, MP3, M4A, FLAC)")
     st.write("Note: Processing long audio files may take several minutes.")
+    
     uploaded_file = st.file_uploader("Choose a file", type=["wav", "mp3", "m4a", "flac"])
     
     if uploaded_file is not None:
@@ -171,7 +186,7 @@ def main():
                 
                 st.subheader("📌 Summary")
                 st.write(results['summary'])
-            
+
         except Exception as e:
             st.error(f"An unexpected error occurred: {str(e)}")
         
@@ -184,4 +199,4 @@ def main():
                     pass
 
 if __name__ == "__main__":
-    main()
+    main()