Update app.py

app.py

@@ -1479,24 +1479,46 @@ if 'query' in st.query_params:
     # Display content or image based on the query
     display_content_or_image(query)
 
-
+def transcribe_canary(filename):
+    from gradio_client import Client
+
+    client = Client("https://awacke1-speech-recognition-canary-nvidiat4.hf.space/")
+    result = client.predict(
+            filename,	# filepath  in 'parameter_5' Audio component
+            "English",	# Literal['English', 'Spanish', 'French', 'German']  in 'Input audio is spoken in:' Dropdown component
+            "English",	# Literal['English', 'Spanish', 'French', 'German']  in 'Transcribe in language:' Dropdown component
+            True,	# bool  in 'Punctuation & Capitalization in transcript?' Checkbox component
+            api_name="/transcribe"
+    )
+    st.write(result)
+    return result
+    
 filename = save_and_play_audio(audio_recorder)
 if filename is not None:
-    transcription = transcribe_audio(filename)
-    try:
-        transcript = transcription['text']
-        st.write(transcript)
+    transcript=''
 
-    except:
-        transcript=''
-        st.write(transcript)
-
-    st.write('Reasoning with your inputs..')
-    response = chat_with_model(transcript)
-    st.write('Response:')
-    st.write(response)
-    filename = generate_filename(response, "txt")
-    create_file(filename, transcript, response, should_save)
+    transcript=transcribe_canary(filename)
+    result = search_arxiv(transcript)
+    result2 = search_glossary(result)
+    #st.markdown(result)
+    #st.markdown
+    
+
+    #transcription = transcribe_audio(filename)
+    #try:
+    #    transcript = transcription['text']
+    #    st.write(transcript)
+
+    #except:
+    #    transcript=''
+    #    st.write(transcript)
+
+    #st.write('Reasoning with your inputs..')
+    #response = chat_with_model(transcript)
+    #st.write('Response:')
+    #st.write(response)
+    #filename = generate_filename(response, "txt")
+    #create_file(filename, transcript, response, should_save)
     
     # Whisper to Llama:
     response = StreamLLMChatResponse(transcript)