Update app.py

app.py

@@ -62,65 +62,65 @@ def ui():
         template=template
     )
         
-    llm_chain = LLMChain(
-        llm = ChatOpenAI(temperature=0.0,model_name='gpt-3.5-turbo'),
-        prompt=prompt,
-        verbose=True,
-        memory=ConversationBufferWindowMemory(k=2)
-    )
-
-    if 'history' not in st.session_state:
-        st.session_state['history'] = []
-
-    if 'generated' not in st.session_state:
-        st.session_state['generated'] = ["Hello ! Ask me anything about " + " 🤗"]
-
-    if 'past' not in st.session_state:
-        st.session_state['past'] = ["Hey ! 👋"]
-
-    if user_api_key is not None and user_api_key.strip() != "":
-        eleven_labs_api_key = st.sidebar.text_input(
-            label="#### Your Eleven Labs API key 👇",
-            placeholder="Paste your Eleven Labs API key",
-            type="password")
-        
-        set_api_key(user_api_key)
-        
-        #container for the chat history
-        response_container = st.container()
-        #container for the user's text input
-        container = st.container()
-
-        with container:
-            with st.form(key='my_form', clear_on_submit=True):
-                audio_file = audiorecorder("Click to record", "Recording...")
-                wav_file = open("./output_audio.mp3", "wb")
-                wav_file.write(audio.tobytes())
-                submit_button = st.form_submit_button(label='Send')
-            if submit_button and audio_file:
-                output_file_path = "./output_audio.mp3"
-                # save_uploaded_file_as_mp3(audio_file,output_file_path )
-                hindi_input_audio,sample_rate=torchaudio.load(output_file_path)
-                #applying the audio recognition 
-                hindi_transcription=parse_transcription('./output_audio.mp3')
-                st.success(f"Audio file saved as {output_file_path}")
-                #convert hindi to english
-                english_input=hindi_to_english(hindi_transcription)
-                #feeding the input to the LLM
-                english_output = conversational_chat(english_input)
-                #converting english to hindi 
-                hin_output=translate_english_to_hindi(english_output)
-                #getting the hindi_tts 
-                hindi_output_audio=hindi_tts(hin_output)
-
-                st.session_state['past'].append(hindi_input_audio)
-                st.session_state['generated'].append(hindi_output_audio)
+        llm_chain = LLMChain(
+            llm = ChatOpenAI(temperature=0.0,model_name='gpt-3.5-turbo'),
+            prompt=prompt,
+            verbose=True,
+            memory=ConversationBufferWindowMemory(k=2)
+        )
+    
+        if 'history' not in st.session_state:
+            st.session_state['history'] = []
+    
+        if 'generated' not in st.session_state:
+            st.session_state['generated'] = ["Hello ! Ask me anything about " + " 🤗"]
+    
+        if 'past' not in st.session_state:
+            st.session_state['past'] = ["Hey ! 👋"]
+    
+        if user_api_key is not None and user_api_key.strip() != "":
+            eleven_labs_api_key = st.sidebar.text_input(
+                label="#### Your Eleven Labs API key 👇",
+                placeholder="Paste your Eleven Labs API key",
+                type="password")
+            
+            set_api_key(user_api_key)
             
-            if st.session_state['generated']:
-                with response_container:
-                    for i in range(len(st.session_state['generated'])):
-                        st.audio(st.session_state["past"][i],format='audio/wav')
-                        st.audio(st.session_state["generated"][i],format='audio/wav')
+            #container for the chat history
+            response_container = st.container()
+            #container for the user's text input
+            container = st.container()
+    
+            with container:
+                with st.form(key='my_form', clear_on_submit=True):
+                    audio_file = audiorecorder("Click to record", "Recording...")
+                    wav_file = open("./output_audio.mp3", "wb")
+                    wav_file.write(audio.tobytes())
+                    submit_button = st.form_submit_button(label='Send')
+                if submit_button and audio_file:
+                    output_file_path = "./output_audio.mp3"
+                    # save_uploaded_file_as_mp3(audio_file,output_file_path )
+                    hindi_input_audio,sample_rate=torchaudio.load(output_file_path)
+                    #applying the audio recognition 
+                    hindi_transcription=parse_transcription('./output_audio.mp3')
+                    st.success(f"Audio file saved as {output_file_path}")
+                    #convert hindi to english
+                    english_input=hindi_to_english(hindi_transcription)
+                    #feeding the input to the LLM
+                    english_output = conversational_chat(english_input)
+                    #converting english to hindi 
+                    hin_output=translate_english_to_hindi(english_output)
+                    #getting the hindi_tts 
+                    hindi_output_audio=hindi_tts(hin_output)
+    
+                    st.session_state['past'].append(hindi_input_audio)
+                    st.session_state['generated'].append(hindi_output_audio)
+                
+                if st.session_state['generated']:
+                    with response_container:
+                        for i in range(len(st.session_state['generated'])):
+                            st.audio(st.session_state["past"][i],format='audio/wav')
+                            st.audio(st.session_state["generated"][i],format='audio/wav')
 
 if __name__ == '__main__':
     ui()