Upload app.py

app.py

@@ -1,122 +1 @@
-import os
-import streamlit as st
-from dotenv import load_dotenv
-from langchain_core.messages import AIMessage, HumanMessage
-from langchain_community.llms import HuggingFaceEndpoint
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.prompts import ChatPromptTemplate
-
-# Load environment variables from .env file
-load_dotenv()
-
-# Get the API token from environment variable
-api_token = os.getenv("API_TOKEN")
-
-# Define the repository ID and task
-repo_id = "mistralai/Mixtral-8x7B-Instruct-v0.1"
-task = "text-generation"
-
-# App config
-st.set_page_config(page_title="GOAHEAD.AI",page_icon= "🌍")
-st.title("GOAHEAD.AI ✈️")
-
-# Define the template outside the function
-template = """
-You are a  travel assistant chatbot your name is GOAHEAD.AI designed to help users plan their trips and provide travel-related information. 
-Here are some scenarios you should be able to handle:
-
-1. Booking Flights: Assist users with booking flights to their desired destinations. 
-Ask for departure city, destination city, travel dates, and any specific preferences (e.g., direct flights, airline preferences). 
-Check available airlines and book the tickets accordingly.
-
-2. Booking Hotels: Help users find and book accommodations. 
-Inquire about city or region, check-in/check-out dates, number of guests, and accommodation preferences (e.g., budget, amenities). 
-
-3. Booking Rental Cars: Facilitate the booking of rental cars for travel convenience. 
-Gather details such as pickup/drop-off locations, dates, car preferences (e.g., size, type), and any additional requirements.
-
-4. Destination Information: Provide information about popular travel destinations. 
-Offer insights on attractions, local cuisine, cultural highlights, weather conditions, and best times to visit.
-
-5. Travel Tips: Offer practical travel tips and advice. 
-Topics may include packing essentials, visa requirements, currency exchange, local customs, and safety tips.
-
-6. Weather Updates: Give current weather updates for specific destinations or regions. 
-Include temperature forecasts, precipitation chances, and any weather advisories.
-
-7. Local Attractions: Suggest local attractions and points of interest based on the user's destination. 
-Highlight must-see landmarks, museums, parks, and recreational activities.
-
-8. Customer Service: Address customer service inquiries and provide assistance with travel-related issues. 
-Handle queries about bookings, cancellations, refunds, and general support.
-
-Please ensure responses are informative, accurate, and tailored to the user's queries and preferences. 
-Use natural language to engage users and provide a seamless experience throughout their travel planning journey.
-
-Chat history:
-{chat_history}
-
-User question:
-{user_question}
-"""
-
-prompt = ChatPromptTemplate.from_template(template)
-
-# Function to get a response from the model
-def get_response(user_query, chat_history):
-    # Initialize the Hugging Face Endpoint
-    llm = HuggingFaceEndpoint(
-        huggingfacehub_api_token=api_token,
-        repo_id=repo_id,
-        task=task
-    )
-
-    chain = prompt | llm | StrOutputParser()
-
-    response = chain.invoke({
-        "chat_history": chat_history,
-        "user_question": user_query,
-    })
-
-    return response
-
-
-# Initialize session state
-if "chat_history" not in st.session_state:
-    st.session_state.chat_history = [
-        AIMessage(content="Hello, how can I help you?"),
-    ]
-    
-# Display chat history
-for message in st.session_state.chat_history:
-    if isinstance(message, AIMessage):
-        with st.chat_message("AI"):
-            st.write(message.content)
-    elif isinstance(message, HumanMessage):
-        with st.chat_message("Human"):
-            st.write(message.content)
-
-# User input
-user_query = st.chat_input("Type your message here...")
-if user_query is not None and user_query != "":
-    st.session_state.chat_history.append(HumanMessage(content=user_query))
-
-    with st.chat_message("Human"):
-        st.markdown(user_query)
-
-    response = get_response(user_query, st.session_state.chat_history)
-
-    # Remove any unwanted prefixes from the response
-    response = (
-        response
-        .replace("AI response:", "")
-        .replace("chat response:", "")
-        .replace("bot response:", "")
-        .replace("Desired Assistant Response:", "")
-        .strip()
-    )
-
-    with st.chat_message("AI"):
-        st.write(response)
-
-    st.session_state.chat_history.append(AIMessage(content=response))
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Define the regular expression pattern for YouTube URLs\n    youtube_regex = (r'(https?://(?:www\\.)?(?:youtube\\.com/watch\\?v=|youtu\\.be/)[^ \\n]+)')\n    # Use re.findall() to find all matches in the text\n    matches = re.findall(youtube_regex, text)\n    return str(' '.join(matches))\n\n\n# Initialize session state\nif \"chat_history\" not in st.session_state:\n    st.session_state.chat_history = [AIMessage(content=\"Hello, how can I help you?\")]\n    \n    \n# Display chat history\nfor message in st.session_state.chat_history:\n    if isinstance(message, AIMessage):\n        with st.chat_message(\"AI\"):\n            st.write(message.content)\n    elif isinstance(message, HumanMessage):\n        with st.chat_message(\"Human\"):\n            st.write(message.content)\n\n            \n# User input\nuser_query = st.chat_input(\"Type your message here...\")\nif user_query is not None and user_query != \"\":\n    st.session_state.chat_history.append(HumanMessage(content=user_query))\n\n    with st.chat_message(\"Human\"):\n        st.markdown(user_query)\n    \n    loader = YoutubeLoader.from_youtube_url(\n        find_youtube_links(user_query),\n        add_video_info=False,\n        language=[\"en\", \"vi\"],\n        translation=\"en\",\n    )\n    docs = loader.load()\n    # Convert the loaded documents to a list of dictionaries\n    data_list = [\n        {\n            \"source\": doc.metadata['source'],\n            \"page_content\": doc.page_content\n        }\n        for doc in docs\n    ]\n\n    df = pd.DataFrame(data_list)\n    loader = DataFrameLoader(df, page_content_column='page_content')\n    content = loader.load()\n    # reviews = filter_complex_metadata(reviews)\n\n    # Split the document into chunks with a specified chunk size\n    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1500, chunk_overlap=150)\n    all_splits = text_splitter.split_documents(content)\n    \n    # Initialize the embedding model\n    embedding_model = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L12-v2\")\n    \n    # Store the document into a vector store with a specific embedding model\n    vectorstore = FAISS.from_documents(all_splits, embedding_model)\n    reviews_retriever  = vectorstore.as_retriever()\n    \n    # Function to get a response from the model\n    def get_response(user_query, chat_history):\n        chain = prompt_template | chat_model | StrOutputParser()\n        response = chain.invoke({\n            \"user_question\": user_query,\n            \"chat_history\": chat_history,\n        })\n        return response\n    \n    response = get_response(reviews_retriever, st.session_state.chat_history)\n    \n    with st.chat_message(\"AI\"):\n        st.write(response)\n\n    st.session_state.chat_history.append(AIMessage(content=response))","metadata":{"execution":{"iopub.status.busy":"2024-07-05T12:18:13.933873Z","iopub.execute_input":"2024-07-05T12:18:13.934280Z","iopub.status.idle":"2024-07-05T12:19:42.733200Z","shell.execute_reply.started":"2024-07-05T12:18:13.934251Z","shell.execute_reply":"2024-07-05T12:19:42.732422Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\nToken is valid (permission: write).\nYour token has been saved to /root/.cache/huggingface/token\nLogin successful\n","output_type":"stream"},{"output_type":"display_data","data":{"text/plain":"Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]","application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"c4d93f1a24df44f2b5b1d6f898ee86d5"}},"metadata":{}}]}]}