Update app.py

app.py

@@ -47,109 +47,89 @@ user_api_key = st.sidebar.text_input(
     type="password")
 
 def ui():
-
+    
+    
     if user_api_key is not None and user_api_key.strip() != "":
-        os.environ["OPENAI_API_KEY"] = user_api_key
+        os.environ["OPENAI_API_KEY"] =user_api_key
         template = """
-            Your custom prompt
+            Your custon promp
             {history}
-            Me: Behave like a Telecomm customer service call agent and don't include any website address, company name, or any other parameter in your output {human_input}
+            Me:Behave like a  Telecomm customer servce call agent and don't include any website address, compnay name or any other parameter in your output {human_input}
             Jack:
-        """
-
+            """
+        
         prompt = PromptTemplate(
-            input_variables=["history", "human_input"],
-            template=template
-        )
-
+        input_variables=["history", "human_input"],
+        template=template
+    )
+        
         llm_chain = LLMChain(
-            llm=ChatOpenAI(temperature=0.0, model_name='gpt-3.5-turbo'),
+            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'] = []
-
+    
         if 'past' not in st.session_state:
             st.session_state['past'] = []
+    
+        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 = st.file_uploader("Upload an audio file ", type=[ "wav,Mp4","Mp3"])
+                    submit_button = st.form_submit_button(label='Send')
+                if audio_file is not None and submit_button :
+                    output_file_path = "./output_audio.mp3"
+                    save_uploaded_file_as_mp3(audio_file,output_file_path )
+                    hindi_input_audio,sample_rate= librosa.load(output_file_path, sr=None, mono=True)
+                    #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(llm_chain,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)
+                    # hindi_output_file="./Hindi_output_Audio.Mp3"
+                    # save_uploaded_file_as_mp3(hindi_out"put_audio,hindi_output_file)
+                    st.audio(hindi_output_audio)
+    
+                    st.session_state['past'].append(hindi_input_audio)
+                    st.session_state['generated'].append(hindi_output_audio)
+                
+            if 'generated' in st.session_state and 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')
 
-        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)
-
-        audio_file = st.file_uploader("Upload an audio file", type=["wav", "mp4", "mp3"])
-        if audio_file is not None:
-            output_file_path = "./output_audio.mp3"
-            save_uploaded_file_as_mp3(audio_file, output_file_path)
-            hindi_input_audio, sample_rate = librosa.load(output_file_path, sr=None, mono=True)
-
-            # Applying 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(llm_chain, english_input)
-
-            # Convert English to Hindi
-            hin_output = translate_english_to_hindi(english_output)
-
-            # Getting the Hindi TTS
-            hindi_output_audio = hindi_tts(hin_output)
-
-            # Show original uploaded audio
-            st.audio(audio_file, format='audio/mp3')
-
-            # Show processed output audio
-            st.audio(hindi_output_audio, format='audio/mp3')
-
-    # st.markdown("---")
-    # # Add a new audio uploader for users to upload another audio file
-    # with st.form(key='my_form', clear_on_submit=True):
-    #     audio_file_new = st.file_uploader("Upload another audio file", type=["wav", "mp4", "mp3"])
-    #     submit_button = st.form_submit_button(label='Process and Play')
-
-    # if audio_file_new is not None and submit_button:
-    #     output_file_path_new = "./output_audio_new.mp3"
-    #     save_uploaded_file_as_mp3(audio_file_new, output_file_path_new)
-    #     hindi_input_audio_new, sample_rate_new = librosa.load(output_file_path_new, sr=None, mono=True)
-
-    #     # Applying audio recognition for the new file
-    #     hindi_transcription_new = parse_transcription(output_file_path_new)
-    #     st.success(f"Audio file saved as {output_file_path_new}")
-
-    #     # Convert Hindi to English for the new file
-    #     english_input_new = hindi_to_english(hindi_transcription_new)
-
-    #     # Feeding the input to the LLM for the new file
-    #     english_output_new = conversational_chat(llm_chain, english_input_new)
-
-    #     # Convert English to Hindi for the new file
-    #     hin_output_new = translate_english_to_hindi(english_output_new)
-
-    #     # Getting the Hindi TTS for the new file
-    #     hindi_output_audio_new = hindi_tts(hin_output_new)
-
-    #     # Show original uploaded audio for the new file
-    #     st.audio(audio_file_new, format='audio/mp3')
-
-    #     # Show processed output audio for the new file
-    #     st.audio(hindi_output_audio_new, format='audio/mp3')
 if __name__ == '__main__':
     ui()
 
             
             
+