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from dotenv import load_dotenv
import time
import gradio as gr
from PIL import Image
from ChatCohere import chat_completion, summarize, card_completion
from PokemonCards import choose_random_cards
from QdrantRag import NeuralSearcher, SemanticCache, qdrant_client, encoder, image_encoder, processor, sparse_encoder
load_dotenv()
searcher = NeuralSearcher("pokemon_texts", "pokemon_images", qdrant_client, encoder, image_encoder, processor, sparse_encoder)
semantic_cache = SemanticCache(qdrant_client, encoder, "semantic_cache", 0.75)
def chat_pokemon(message: str, history):
answer = semantic_cache.search_cache(message)
if answer != "":
r = ""
for c in answer:
r += c
time.sleep(0.001)
yield r
else:
context_search = searcher.search_text(message)
reranked_context = searcher.reranking(message, context_search)
context = "\n\n-----------------\n\n".join(reranked_context)
response = chat_completion(message, "Context:\n\n"+context)
semantic_cache.upload_to_cache(message, response)
r = ""
for c in response:
r += c
time.sleep(0.001)
yield r
def what_pokemon(image_input):
save_path = Image.fromarray(image_input)
result = searcher.search_image(save_path)
results = "\n".join(result)
return "You Pokemon might be:\n" + results
def card_package(n_cards:int=5):
description, cards = choose_random_cards(n_cards)
package = [f"" for i in range(len(cards))]
cards_message = "\n\n".join(package)
natural_lang_description = card_completion(f"Can you enthusiastically describe the cards in this package?\n\n{description}")
return "## Your package:\n\n" + cards_message + "\n\n## Description:\n\n" + summarize(natural_lang_description)
iface1 = gr.ChatInterface(fn=chat_pokemon, title="Pokemon Chatbot", description="Ask any question about Pokemon and get an answer!")
iface2 = gr.Interface(fn=what_pokemon, title="Pokemon Image Classifier", description="Upload an image of a Pokemon and get its name!", inputs="image", outputs="text")
iface3 = gr.Interface(fn=card_package, title="Pokemon Card Package", description="Get a package of random Pokemon cards!", inputs=gr.Slider(5,10,step=1), outputs=gr.Markdown(value="Your output will be displayed here", label="Card Package"))
iface = gr.TabbedInterface([iface1, iface2, iface3], ["PokemonChat", "Identify Pokemon", "Card Package"])
iface.launch(server_name="0.0.0.0", server_port=7860)
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