Update app.py
Browse files
app.py
CHANGED
@@ -28,14 +28,19 @@ set_global_service_context(service_context)
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# Transcribe function
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def transcribe_video(youtube_url):
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# Streamlit UI
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st.title("YouTube Video Chatbot")
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@@ -47,32 +52,26 @@ if youtube_url and "query_engine" not in st.session_state:
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st.write("Transcribing video... Please wait.")
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st.session_state.query_engine = transcribe_video(youtube_url)
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# Chatbot UI
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if "messages" not in st.session_state:
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st.session_state.messages = []
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# Display chat history
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for message in st.session_state.messages:
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if message["role"] ==
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st.
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else:
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st.write(f"Chatbot: {message['content']}")
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# User input
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prompt = st.
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# Add user message to chat history
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st.session_state.messages.append({"role": "human", "content": prompt})
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# Get response from the chatbot
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response = st.session_state.query_engine.query(prompt)
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response_text = response.response
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# Add assistant response to chat history
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st.session_state.messages.append({"role": "assistant", "content":
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# Refresh the page to show the updated chat history
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if prompt:
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st.experimental_rerun()
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# Transcribe function
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def transcribe_video(youtube_url):
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with st.status("Starting client"):
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client = Client("https://sanchit-gandhi-whisper-jax.hf.space/")
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st.write("Requesting client")
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with st.status("Requesting Whisper"):
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result = client.predict(youtube_url, "transcribe", True, fn_index=7)
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st.write("Requesting API...")
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with open(f'{PATH}/docs.txt','w') as f:
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f.write(result[1])
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st.write('Writing File...')
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with st.status("Requesting Embeddings"):
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documents = SimpleDirectoryReader(PATH).load_data()
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index = VectorStoreIndex.from_documents(documents)
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return index.as_query_engine()
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# Streamlit UI
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st.title("YouTube Video Chatbot")
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st.write("Transcribing video... Please wait.")
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st.session_state.query_engine = transcribe_video(youtube_url)
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if "messages" not in st.session_state:
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st.session_state.messages = []
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# Display chat messages from history on app rerun
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for message in st.session_state.messages:
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with st.chat_message(message["role"], avatar=("π§βπ»" if message["role"] == 'human' else 'π¦')):
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st.markdown(message["content"])
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# User input
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prompt = st.chat_input("Ask something about the video:")
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if prompt := textinput and "query_engine" in st.session_state:
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# Display user message in chat message container
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st.chat_message("human",avatar = "π§βπ»").markdown(prompt)
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# Add user message to chat history
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st.session_state.messages.append({"role": "human", "content": prompt})
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response = st.session_state.query_engine.query(prompt)
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response_text = response.response
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with st.chat_message("assistant", avatar='π¦'):
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st.markdown(response_text)
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# Add assistant response to chat history
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st.session_state.messages.append({"role": "assistant", "content": response})
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