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Update app.py
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app.py
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@@ -5,8 +5,20 @@ handling the Gradio/Streamlit UI and orchestrating the RAG pipeline
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to answer queries based on the game's mechanics and manual.
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"""
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import gradio as gr
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from helpers import
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import pytest
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import sys
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@@ -20,7 +32,11 @@ else:
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# Initialiations and globals
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print(f"Loading {CHAT_MODEL_ID} model...")
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chat_model, tokenizer = load_chat_model()
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print("
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MANUAL_PATH = "emerald_manual.txt"
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print(f"Loading vector store from {MANUAL_PATH}...")
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@@ -29,7 +45,8 @@ print("Vector store loaded!")
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def respond(message, history):
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print("Enter app.respond...")
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context = find_similar_documents(
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prompt = create_prompt(message, history, context)
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response = generate_answer(chat_model, tokenizer, prompt)
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if not response:
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to answer queries based on the game's mechanics and manual.
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"""
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import gradio as gr
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from helpers import (
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create_prompt,
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find_similar_documents,
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load_vector_store,
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CHROMA_PATH,
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COLLECTION_NAME,
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)
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from model import (
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CHAT_MODEL_ID,
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EMBEDDING_MODEL_ID,
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generate_answer,
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load_chat_model,
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load_embedding_model,
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)
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import pytest
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import sys
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# Initialiations and globals
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print(f"Loading {CHAT_MODEL_ID} model...")
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chat_model, tokenizer = load_chat_model()
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print("Chat model loaded!")
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print(f"Loading {EMBEDDING_MODEL_ID} model...")
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embedding_model = load_embedding_model()
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print("Embedding model loaded!"
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MANUAL_PATH = "emerald_manual.txt"
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print(f"Loading vector store from {MANUAL_PATH}...")
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def respond(message, history):
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print("Enter app.respond...")
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context = find_similar_documents(
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CHROMA_PATH, COLLECTION_NAME, embedding_model, message, k=5)
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prompt = create_prompt(message, history, context)
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response = generate_answer(chat_model, tokenizer, prompt)
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if not response:
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