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Update app.py
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app.py
CHANGED
@@ -8,6 +8,7 @@ import shutil
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import os
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import time
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icons = {"assistant": "robot.png", "user": "man-kddi.png"}
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# Configure the Llama index settings
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@@ -16,6 +17,7 @@ Settings.llm = HuggingFaceInferenceAPI(
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tokenizer_name="meta-llama/Meta-Llama-3-8B-Instruct",
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context_window=3900,
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token=os.getenv("HF_TOKEN"),
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generate_kwargs={"temperature": 0.1},
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)
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Settings.embed_model = HuggingFaceEmbedding(
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@@ -37,20 +39,27 @@ def data_ingestion():
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index.storage_context.persist(persist_dir=PERSIST_DIR)
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def remove_old_files():
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directory_path = "data"
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shutil.rmtree(directory_path)
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os.makedirs(directory_path)
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def extract_transcript_details(youtube_video_url):
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try:
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video_id
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transcript = ""
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for i in transcript_text:
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transcript += " " + i["text"]
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return transcript
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except Exception as e:
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st.error(e)
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@@ -58,20 +67,21 @@ def handle_query(query):
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storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
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index = load_index_from_storage(storage_context)
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chat_text_qa_msgs = [
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]
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text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
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query_engine = index.as_query_engine(text_qa_template=text_qa_template)
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answer = query_engine.query(query)
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if hasattr(answer, 'response'):
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return answer.response
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elif isinstance(answer, dict) and 'response' in answer:
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@@ -84,6 +94,7 @@ def streamer(text):
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yield i
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time.sleep(0.001)
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# Streamlit app initialization
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st.title("Chat with your PDF📄")
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st.markdown("**Built by [Pachaiappan❤️](https://mr-vicky-01.github.io/Portfolio/)**")
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@@ -101,7 +112,7 @@ with st.sidebar:
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video_url = st.text_input("Enter Youtube Video Link: ")
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if st.button("Submit & Process"):
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with st.spinner("Processing..."):
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if len(os.listdir("data")) !=
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remove_old_files()
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if uploaded_file:
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@@ -128,4 +139,4 @@ if user_prompt and (uploaded_file or video_url):
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with st.spinner("Thinking..."):
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response = handle_query(user_prompt)
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with st.chat_message("user", avatar="robot.png"):
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st.write_stream(streamer(response))
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import os
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import time
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icons = {"assistant": "robot.png", "user": "man-kddi.png"}
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# Configure the Llama index settings
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tokenizer_name="meta-llama/Meta-Llama-3-8B-Instruct",
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context_window=3900,
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token=os.getenv("HF_TOKEN"),
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# max_new_tokens=1000,
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generate_kwargs={"temperature": 0.1},
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)
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Settings.embed_model = HuggingFaceEmbedding(
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index.storage_context.persist(persist_dir=PERSIST_DIR)
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def remove_old_files():
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# Specify the directory path you want to clear
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directory_path = "data"
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# Remove all files and subdirectories in the specified directory
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shutil.rmtree(directory_path)
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# Recreate an empty directory if needed
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os.makedirs(directory_path)
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def extract_transcript_details(youtube_video_url):
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try:
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video_id=youtube_video_url.split("=")[1]
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transcript_text=YouTubeTranscriptApi.get_transcript(video_id)
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transcript = ""
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for i in transcript_text:
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transcript += " " + i["text"]
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return transcript
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except Exception as e:
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st.error(e)
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storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
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index = load_index_from_storage(storage_context)
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chat_text_qa_msgs = [
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(
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"user",
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"""You are Q&A assistant named CHATTO, created by Pachaiappan [linkdin](https://www.linkedin.com/in/pachaiappan) an AI Specialist. Your main goal is to provide answers as accurately as possible, based on the instructions and context you have been given. If a question does not match the provided context or is outside the scope of the document, you only say the user to 'Please ask a questions within the context of the document'.
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Context:
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{context_str}
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Question:
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{query_str}
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"""
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)
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]
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text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
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query_engine = index.as_query_engine(text_qa_template=text_qa_template)
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answer = query_engine.query(query)
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if hasattr(answer, 'response'):
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return answer.response
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elif isinstance(answer, dict) and 'response' in answer:
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yield i
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time.sleep(0.001)
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# Streamlit app initialization
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st.title("Chat with your PDF📄")
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st.markdown("**Built by [Pachaiappan❤️](https://mr-vicky-01.github.io/Portfolio/)**")
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video_url = st.text_input("Enter Youtube Video Link: ")
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if st.button("Submit & Process"):
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with st.spinner("Processing..."):
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if len(os.listdir("data")) !=0:
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remove_old_files()
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if uploaded_file:
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with st.spinner("Thinking..."):
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response = handle_query(user_prompt)
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with st.chat_message("user", avatar="robot.png"):
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st.write_stream(streamer(response))
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