Spaces:
Runtime error
Runtime error
added url button to steam
Browse files- Home.py +81 -34
- module/__custom__.py +9 -0
Home.py
CHANGED
@@ -1,10 +1,13 @@
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import streamlit as st
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import os
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import random
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import time
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from module.__custom__ import *
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from streamlit_extras.switch_page_button import switch_page
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# Openai API Key
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import openai
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@@ -26,6 +29,7 @@ def read_api_key_from_secrets(file_path='secrets.json'):
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# Example usage
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try:
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openai.api_key = os.environ['key']
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os.environ['OPENAI_API_KEY'] = os.environ['key']
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print(f"OpenAI API Key Found")
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@@ -55,13 +59,12 @@ db_plot = Chroma(
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embedding_function=embedding
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)
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-
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with st.sidebar: is_plot = st.toggle('Enable Plot')
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db_selected = db_cos
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if is_plot: db_selected = db_plot
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-
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from langchain.agents.agent_toolkits.conversational_retrieval.tool import (
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create_retriever_tool,
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)
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@@ -71,8 +74,30 @@ retriever_tool = create_retriever_tool(
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"document-retriever",
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"Query a retriever to get information about the video game dataset.",
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)
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-
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from langchain.utils.openai_functions import convert_pydantic_to_openai_function
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from pydantic import BaseModel, Field
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@@ -91,8 +116,8 @@ class Response(BaseModel):
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description="A list of the names of the games found for the user. Only include the game name if it was given as a result to the user's query."
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)
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import json
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from langchain.schema.agent import AgentActionMessageLog, AgentFinish
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def parse(output):
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# If no function was invoked, return to user
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@@ -149,16 +174,19 @@ agent = (
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agent_executor = AgentExecutor(tools=[retriever_tool], agent=agent, verbose=True)
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post_prompt = """
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Respond with a respectable and friendy tone.
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Do not give me any information that is not included in the document.
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If you
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If you need more context from the user, ask them to provide more context in the next query. Do not include games that contain the queried game in the title.
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If a user asks for a type of game, use that type to find a game that mentions the type.
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If a user asks for a specific number of games, and you cannot provide that, answer with what games you found and explain why you could not find others.
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"""
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st.header("🕹️ GameInsightify
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# Description for users
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st.markdown("""
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@@ -175,7 +203,7 @@ if 'gamenames' not in st.session_state:
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# Slider on range and button to clear chat history
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col1, col2= st.columns([8,2])
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with col1:
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-
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with col2:
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if st.button("Clear chat"):
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st.session_state.messages = []
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with st.chat_message("assistant"): # Display assistant response in chat message container
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message_placeholder = st.empty()
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# docs = db.max_marginal_relevance_search(prompt,k=query_num, fetch_k=10) # Sending query to db
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-
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-
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) # retrieve response from chatgpt
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-
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top_games = []
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assistant_response = ""
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# for idx, doc in enumerate(docs['name']):
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# gamename = doc
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# top_games.append(gamename)
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# assistant_response += f"{idx+1}. {gamename}\n"
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print(docs)
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assistant_response += docs["answer"]
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except:
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assistant_response += docs["output"]
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# separating response into chunk of words
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chunks = []
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for line in assistant_response.splitlines():
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# Add assistant response to chat history
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st.session_state.messages.append({"role": "assistant", "content": full_response})
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if is_plot: st.session_state.gamenames.append(
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col1, col2, col3= st.columns([4,
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with col2:
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if is_plot and db_selected==db_plot:
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if st.button("Plot Games"): # button in center column
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switch_page('Overall')
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-
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# Styling on Tabs
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css = '''
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import streamlit as st
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import pandas as pd
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import os
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import random
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import time
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from module.__custom__ import *
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from streamlit_extras.switch_page_button import switch_page
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df = pd.read_csv('./data/cosine.csv')
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# Openai API Key
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import openai
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# Example usage
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try:
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openai.api_key = os.environ['key']
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os.environ['OPENAI_API_KEY'] = os.environ['key']
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print(f"OpenAI API Key Found")
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embedding_function=embedding
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)
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with st.sidebar: is_plot = st.toggle('Enable Plot')
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db_selected = db_cos
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if is_plot: db_selected = db_plot
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##### Conversational Retrieval #####
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from langchain.agents.agent_toolkits.conversational_retrieval.tool import (
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create_retriever_tool,
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)
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"document-retriever",
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"Query a retriever to get information about the video game dataset.",
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)
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##################################
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##### Retriever - Self Query #####
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metadata_field_info = [
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AttributeInfo(
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name="name",
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description="The name of the video game on steam",
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type="string",
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)
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]
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document_content_description = "Brief summary of a video game on Steam"
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retriever_plot = SelfQueryRetriever.from_llm(
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llm,
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db_selected,
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document_content_description,
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metadata_field_info,
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enable_limit=True,
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)
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##################################
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from typing import List
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from langchain.utils.openai_functions import convert_pydantic_to_openai_function
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from pydantic import BaseModel, Field
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description="A list of the names of the games found for the user. Only include the game name if it was given as a result to the user's query."
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)
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import json
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from langchain.schema.agent import AgentActionMessageLog, AgentFinish
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def parse(output):
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# If no function was invoked, return to user
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agent_executor = AgentExecutor(tools=[retriever_tool], agent=agent, verbose=True)
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post_prompt = """
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1. Respond with a respectable and friendy tone.
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2. You should give the best possible answer based on user's query.
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3. Do not give me any information that is not included in the document.
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4. If you are able to, provide the links to the steam site for the games answer.
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5. If you need more context from the user, ask them to provide more context in the next query. Do not include games that contain the queried game in the title.
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6. If a user asks for a type of game, use that type to find a game that mentions the type.
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"""
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# If you do not have an answer, your response should be kind and apologetic, as to why you do not have an answer.
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# If a user asks for a specific number of games, and you cannot provide that, answer with what games you found and explain why you could not find others.
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st.header("🕹️ GameInsightify")
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st.title("Your Personal :green[Game Recommender]")
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st.image('./data/img/demoGIF.gif')
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# Description for users
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st.markdown("""
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# Slider on range and button to clear chat history
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col1, col2= st.columns([8,2])
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with col1:
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pass
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with col2:
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if st.button("Clear chat"):
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st.session_state.messages = []
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with st.chat_message("assistant"): # Display assistant response in chat message container
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message_placeholder = st.empty()
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assistant_response = ""
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full_response = ""
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# docs = db.max_marginal_relevance_search(prompt,k=query_num, fetch_k=10) # Sending query to db
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if is_plot:
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docs = retriever_plot.invoke(prompt)
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full_response = random.choice( # 1st sentence of response
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["I recommend the following games:\n",
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f"Hi, human! These are the {len(docs)} best games:\n",
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f"I bet you will love these {len(docs)} games:\n",]
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)
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# formatting response from db
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top_games = []
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for idx, doc in enumerate(docs):
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gamename = doc.metadata['name']
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top_games.append(gamename)
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assistant_response += f"{idx+1}. {gamename}\n"
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else:
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docs = agent_executor.invoke(
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{"input": f"{prompt} {post_prompt}"},
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return_only_outputs=True,
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) # retrieve response from chatgpt
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try:
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assistant_response += docs["answer"]
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except:
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assistant_response += docs["output"]
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top_games = docs['name']
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print(docs)
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# separating response into chunk of words
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chunks = []
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for line in assistant_response.splitlines():
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# Add assistant response to chat history
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st.session_state.messages.append({"role": "assistant", "content": full_response})
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if is_plot: st.session_state.gamenames.append(top_games)
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col1, col2, col3= st.columns([4,3,4])
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with col2:
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if is_plot and db_selected==db_plot:
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if st.button("Plot Games"): # button in center column
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switch_page('Overall')
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else:
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try:
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appid = df[df['Name']==top_games[0]]['AppID'].iloc[0]
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url = f'https://store.steampowered.com/app/{appid}'
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st.link_button("Check on Steam", url)
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except: pass
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with st.sidebar:
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try: home_dfbox(top_games)
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except: pass
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# Styling on Tabs
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css = '''
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module/__custom__.py
CHANGED
@@ -121,6 +121,15 @@ def rec_dfbox():
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df_names = pd.DataFrame(rec_games, columns=['gamename'])
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st.write(title)
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st.dataframe(df_names[0:len(rec_games)])
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# plot 1 Section
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"""Plot contains the top ranked games
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df_names = pd.DataFrame(rec_games, columns=['gamename'])
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st.write(title)
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st.dataframe(df_names[0:len(rec_games)])
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# Overloaded with argument of names
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def home_dfbox(rec_games):
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title = f"1.1 :blue[Recommended] by :green[GameInsightify]"
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if len(rec_games) > 0:
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with st.sidebar:
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df_names = pd.DataFrame(rec_games, columns=['gamename'])
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st.write(title)
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st.dataframe(df_names[0:len(rec_games)])
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# plot 1 Section
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"""Plot contains the top ranked games
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