Create app.py

app.py

@@ -0,0 +1,54 @@
+from dotenv import load_dotenv
+import time
+import gradio as gr
+from PIL import Image
+from ChatCohere import chat_completion, summarize
+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):
+    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)
+        final_prompt = f"USER QUERY:\n\n{message.content}\n\nCONTEXT:\n\n{context}"
+        response = chat_completion(final_prompt)
+        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)
+    return "You Pokemon might be: " + result[0]
+
+def card_package(n_cards:int=5):
+    description, cards = choose_random_cards(n_cards)
+    package = [f"![Card {i+1}]({cards[i]})" for i in range(len(cards))]
+    cards_message = "\n\n".join(package)
+    natural_lang_description = chat_completion([{"role": "system", "content": "You are an expert in Pokemon cards. You know everything about their characteristics, abilities, and evolutions. You always reply with the most accurate information, you are polite and helpful."}, {"role": "user", "content": 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()