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| import os | |
| os.system('pip install curl_cffi tqdm bitsandbytes tiktoken g4f pinecone-client pandas datasets sentence-transformers') | |
| # Setup and load your keys | |
| import os | |
| from g4f import ChatCompletion | |
| #from google.colab import userdata | |
| from pinecone import Pinecone | |
| import pandas as pd | |
| from datasets import Dataset | |
| from sentence_transformers import SentenceTransformer | |
| import gradio as gr | |
| model_name = "BAAI/bge-m3" | |
| # APIs personales | |
| #PINECONE_ENVIRONMENT = us-east-1 | |
| #PINECONE_API_KEY = 3a3e9022-381d-436e-84cb-ba93464d283e | |
| #os.environ["PINECONE_ENVIRONMENT"] = "us-east-1" | |
| #os.environ["PINECONE_API_KEY"] = "3a3e9022-381d-436e-84cb-ba93464d283e" | |
| # Retrieve the Pinecone API key from the user | |
| PINECONE_API_KEY = "3a3e9022-381d-436e-84cb-ba93464d283e" # Use the key you set in the secrets | |
| PINECONE_ENVIRONMENT = "us-east-1" # Use the environment you set in the secrets | |
| # Initialize Pinecone with the API key | |
| pc = Pinecone(api_key=PINECONE_API_KEY) | |
| # Global variables to store the selected model and dimensions | |
| EMBED_MODEL = 'BGE_M3-1024' | |
| DIMENSIONS = 1024 | |
| # Confirm selection automatically | |
| print(f"Model selected: {EMBED_MODEL}") | |
| print(f"Dimensions set as: {DIMENSIONS}") | |
| # Function to print current selection (can be used in other cells) | |
| def print_current_selection(): | |
| print(f"Currently selected model: {EMBED_MODEL}") | |
| print(f"Dimensions: {DIMENSIONS}") | |
| # Establecer el nombre del índice automáticamente | |
| INDEX_NAME = 'vestidos' | |
| # Obtener la clave API de Pinecone | |
| #PINECONE_API_KEY = userdata.get('PINECONE_API_KEY') | |
| def connect_to_pinecone(index_name): | |
| global INDEX_NAME | |
| try: | |
| pc = Pinecone(api_key=PINECONE_API_KEY) | |
| index = pc.Index(index_name) | |
| # Asegurarse de que la conexión se establezca | |
| index_stats = index.describe_index_stats() | |
| print(f"Successfully connected to Pinecone index '{index_name}'!") | |
| print("Index Stats:", index_stats) | |
| # Actualizar la variable global INDEX_NAME | |
| INDEX_NAME = index_name | |
| print(f"Global INDEX_NAME updated to: {INDEX_NAME}") | |
| except Exception as e: | |
| print(f"Failed to connect to Pinecone index '{index_name}':", str(e)) | |
| # Conectar automáticamente al índice "vestidos" | |
| connect_to_pinecone(INDEX_NAME) | |
| # Función para imprimir el nombre del índice actual (puede ser usada en otras celdas) | |
| def print_current_index(): | |
| print(f"Current index name: {INDEX_NAME}") | |
| # Verificar si las variables globales necesarias están configuradas | |
| if 'INDEX_NAME' not in globals() or INDEX_NAME is None: | |
| raise ValueError("INDEX_NAME is not set. Please set the index name first.") | |
| if 'EMBED_MODEL' not in globals() or EMBED_MODEL is None: | |
| raise ValueError("EMBED_MODEL is not set. Please select an embedding model first.") | |
| # Inicializar cliente de Pinecone | |
| #PINECONE_API_KEY = userdata.get('PINECONE_API_KEY') | |
| pc = Pinecone(api_key=PINECONE_API_KEY) | |
| # Inicializar el índice de Pinecone | |
| index = pc.Index(INDEX_NAME) | |
| # Obtener la dimensión del índice | |
| index_stats = index.describe_index_stats() | |
| vector_dim = index_stats['dimension'] | |
| print(f"Index dimension: {vector_dim}") | |
| # Definir manualmente los campos de contexto y enlace | |
| CONTEXT_FIELDS = ['Etiqueta', 'Pregunta 1', 'Pregunta 2', 'Pregunta 3', 'Respuesta Combinada'] | |
| LINK_FIELDS = ['Etiqueta', 'Respuesta Combinada'] | |
| # Imprimir confirmación de campos seleccionados | |
| print(f"Context fields set to: {CONTEXT_FIELDS}") | |
| print(f"Link fields set to: {LINK_FIELDS}") | |
| # Función para obtener las selecciones actuales de campos (puede ser usada en otras celdas) | |
| def get_field_selections(): | |
| return { | |
| "CONTEXT_FIELDS": CONTEXT_FIELDS, | |
| "LINK_FIELDS": LINK_FIELDS | |
| } | |
| ##################################### | |
| # Check if required global variables are set | |
| if 'EMBED_MODEL' not in globals() or EMBED_MODEL is None: | |
| raise ValueError("EMBED_MODEL is not set. Please select an embedding model first.") | |
| if 'INDEX_NAME' not in globals() or INDEX_NAME is None: | |
| raise ValueError("INDEX_NAME is not set. Please create or select an index first.") | |
| if 'CONTEXT_FIELDS' not in globals() or 'LINK_FIELDS' not in globals(): | |
| raise ValueError("CONTEXT_FIELDS and LINK_FIELDS are not set. Please run the field selection cell first.") | |
| # Initialize the Sentence-Transformer model | |
| embedding_model = SentenceTransformer(model_name) | |
| # Initialize Pinecone with the API key and connect to the index | |
| pinecone_client = Pinecone(api_key=PINECONE_API_KEY) | |
| index = pinecone_client.Index(INDEX_NAME) | |
| # Constants | |
| LIMIT = 3750 | |
| def vector_search(query): | |
| # Generate embedding using Sentence-Transformer model | |
| xq = embedding_model.encode(query) | |
| # Perform vector search on Pinecone index | |
| res = index.query(vector=xq.tolist(), top_k=3, include_metadata=True) | |
| if res['matches']: | |
| return [ | |
| { | |
| 'content': ' '.join(f"{k}: {v}" for k, v in match['metadata'].items() if k in CONTEXT_FIELDS and k != 'Etiqueta'), | |
| 'metadata': match['metadata'] | |
| } | |
| for match in res['matches'] | |
| if 'metadata' in match | |
| ] | |
| return [] | |
| def create_prompt(query, contexts): | |
| prompt_start = "\n\nContexto:\n" | |
| prompt_end = f"\n\nPregunta: {query}\nRespuesta:" | |
| current_contexts = "\n\n---\n\n".join([context['content'] for context in contexts]) | |
| if len(prompt_start + current_contexts + prompt_end) >= LIMIT: | |
| # Truncate contexts if they exceed the limit | |
| available_space = LIMIT - len(prompt_start) - len(prompt_end) | |
| truncated_contexts = current_contexts[:available_space] | |
| return prompt_start + truncated_contexts + prompt_end | |
| else: | |
| return prompt_start + current_contexts + prompt_end | |
| def complete(prompt): | |
| return [f"Hola"] | |
| def check_image_exists(filepath): | |
| return os.path.exists(filepath) | |
| def chat_function(message, history): | |
| # Perform vector search | |
| search_results = vector_search(message) | |
| # Create prompt with relevant contexts | |
| query_with_contexts = create_prompt(message, search_results) | |
| # Generate response | |
| response = complete(query_with_contexts) | |
| partial_message = response[0].split("\n")[0] # Solo tomar la primera línea de la respuesta | |
| # Handle the logic for processing tags and images internally | |
| relevant_links = [result['metadata'].get(field) for result in search_results for field in LINK_FIELDS if field in result['metadata']] | |
| full_response = partial_message | |
| image_url = None | |
| tags_detected = [] | |
| filtered_links = [] | |
| if relevant_links: | |
| for link in relevant_links: | |
| if any(tag in link for tag in ["lila_61", "lila_63", "lila_62", "lila_64", "fuxia_70", "fuxia_71", "fuxia_72", "fuxia_73", "fuxia_74", "melon_68", "melon_66", "melon_67", "melon_65", "vino_19", "vino_20", "barney_69", "loro_27", "lacre_02", "amarillo_03", "amarillo_04", "azulino_11", "azulino_14", "azulino_12", "azulino_13", "beigs_09", "beigs_10", "beigs_07", "beigs_06", "beigs_08", "beigs_05", "marina_32", "marina_29", "marina_28", "marina_31", "marina_30", "rojo_26", "rojo_23", "rojo_21", "rojo_22", "rojo_25", "rojo_24", "celeste_40", "celeste_38", "celeste_39", "celeste_33", "celeste_35", "celeste_37", "celeste_41", "celeste_42", "celeste_34", "celeste_36", "sirenita_01", "marino_18", "marino_17", "marino_16", "marino_15", "rosa_87", "rosa_86", "rosa_79", "rosa_82", "rosa_83", "rosa_78", "rosa_84", "rosa_85", "rosa_75", "rosa_80", "rosa_81", "rosa_77", "rosa_76", "blanco_55", "blanco_56", "blanco_53", "blanco_52", "blanco_57", "blanco_49", "blanco_51", "blanco_60", "blanco_47", "blanco_44", "blanco_50", "blanco_48", "blanco_59", "blanco_43", "blanco_58", "blanco_46", "blanco_45", "blanco_54"]): | |
| tags_detected.append(link) # Save the tag but don't display it | |
| else: | |
| filtered_links.append(link) | |
| # Add the first relevant link under a single "Respuestas relevantes" section | |
| if filtered_links: | |
| full_response += f".\n\nTe detallamos nuestro contenido a continuación:\n" + filtered_links[0] | |
| # Now handle the images based on the detected tags | |
| tags_to_images = { | |
| "lila_61": "images/lila_61.jpeg", | |
| "lila_63": "images/lila_63.jpeg", | |
| "lila_62": "images/lila_62.jpeg", | |
| "lila_64": "images/lila_64.jpeg", | |
| "fuxia_70": "images/fuxia_70.jpeg", | |
| "fuxia_71": "images/fuxia_71.jpeg", | |
| "fuxia_72": "images/fuxia_72.jpeg", | |
| "fuxia_73": "images/fuxia_73.jpeg", | |
| "fuxia_74": "images/fuxia_74.jpeg", | |
| "melon_68": "images/melon_68.jpeg", | |
| "melon_66": "images/melon_66.jpeg", | |
| "melon_67": "images/melon_67.jpeg", | |
| "melon_65": "images/melon_65.jpeg", | |
| "vino_19": "images/vino_19.jpeg", | |
| "vino_20": "images/vino_20.jpeg", | |
| "barney_69": "images/barney_69.jpeg", | |
| "loro_27": "images/loro_27.png", | |
| "lacre_02": "images/lacre_02.jpeg", | |
| "amarillo_03": "images/amarillo_03.jpeg", | |
| "amarillo_04": "images/amarillo_04.jpeg", | |
| "azulino_11": "images/azulino_11.jpeg", | |
| "azulino_14": "images/azulino_14.jpeg", | |
| "azulino_12": "images/azulino_12.jpeg", | |
| "azulino_13": "images/azulino_13.jpeg", | |
| "beigs_09": "images/beigs_09.jpeg", | |
| "beigs_10": "images/beigs_10.jpeg", | |
| "beigs_07": "images/beigs_07.jpeg", | |
| "beigs_06": "images/beigs_06.jpeg", | |
| "beigs_08": "images/beigs_08.jpeg", | |
| "beigs_05": "images/beigs_05.jpeg", | |
| "marina_32": "images/marina_32.jpeg", | |
| "marina_29": "images/marina_29.jpeg", | |
| "marina_28": "images/marina_28.jpeg", | |
| "marina_31": "images/marina_31.jpeg", | |
| "marina_30": "images/marina_30.jpeg", | |
| "rojo_26": "images/rojo_26.jpeg", | |
| "rojo_23": "images/rojo_23.jpeg", | |
| "rojo_21": "images/rojo_21.jpeg", | |
| "rojo_22": "images/rojo_22.jpeg", | |
| "rojo_25": "images/rojo_25.jpeg", | |
| "rojo_24": "images/rojo_24.jpeg", | |
| "celeste_40": "images/celeste_40.jpeg", | |
| "celeste_38": "images/celeste_38.jpeg", | |
| "celeste_39": "images/celeste_39.jpeg", | |
| "celeste_33": "images/celeste_33.jpeg", | |
| "celeste_35": "images/celeste_35.jpeg", | |
| "celeste_37": "images/celeste_37.jpeg", | |
| "celeste_41": "images/celeste_41.jpeg", | |
| "celeste_42": "images/celeste_42.jpeg", | |
| "celeste_34": "images/celeste_34.jpeg", | |
| "celeste_36": "images/celeste_36.jpeg", | |
| "sirenita_01": "images/sirenita_01.png", | |
| "marino_18": "images/marino_18.jpeg", | |
| "marino_17": "images/marino_17.jpeg", | |
| "marino_16": "images/marino_16.jpeg", | |
| "marino_15": "images/marino_15.jpeg", | |
| "rosa_87": "images/rosa_87.jpeg", | |
| "rosa_86": "images/rosa_86.png", | |
| "rosa_79": "images/rosa_79.jpeg", | |
| "rosa_82": "images/rosa_82.png", | |
| "rosa_83": "images/rosa_83.jpeg", | |
| "rosa_78": "images/rosa_78.jpeg", | |
| "rosa_84": "images/rosa_84.jpeg", | |
| "rosa_85": "images/rosa_85.jpeg", | |
| "rosa_75": "images/rosa_75.jpeg", | |
| "rosa_80": "images/rosa_80.png", | |
| "rosa_81": "images/rosa_81.png", | |
| "rosa_77": "images/rosa_77.jpeg", | |
| "rosa_76": "images/rosa_76.png", | |
| "blanco_55": "images/blanco_55.jpeg", | |
| "blanco_56": "images/blanco_56.jpeg", | |
| "blanco_53": "images/blanco_53.jpeg", | |
| "blanco_52": "images/blanco_52.jpeg", | |
| "blanco_57": "images/blanco_57.jpeg", | |
| "blanco_49": "images/blanco_49.jpeg", | |
| "blanco_51": "images/blanco_51.jpeg", | |
| "blanco_60": "images/blanco_60.jpeg", | |
| "blanco_47": "images/blanco_47.jpeg", | |
| "blanco_44": "images/blanco_44.jpeg", | |
| "blanco_50": "images/blanco_50.jpeg", | |
| "blanco_48": "images/blanco_48.jpeg", | |
| "blanco_59": "images/blanco_59.jpeg", | |
| "blanco_43": "images/blanco_43.jpeg", | |
| "blanco_58": "images/blanco_58.png", | |
| "blanco_46": "images/blanco_46.jpeg", | |
| "blanco_45": "images/blanco_45.jpeg", | |
| "blanco_54": "images/blanco_54.jpeg", | |
| } | |
| for tag in tags_detected: | |
| for key, path in tags_to_images.items(): | |
| if key in tag and check_image_exists(path): | |
| image_url = path | |
| break | |
| return full_response, image_url | |
| def update_image(image_url): | |
| if image_url: | |
| return image_url | |
| else: | |
| return None | |
| # Gradio layout setup | |
| with gr.Blocks() as demo: | |
| with gr.Row(): | |
| with gr.Column(scale=1): | |
| chatbot_input = gr.Textbox(label="Tu mensaje") | |
| chatbot_output = gr.Chatbot(label="ChatBot") | |
| chatbot_history = gr.State(value=[]) | |
| image_url = gr.State(value=None) | |
| submit_button = gr.Button("Enviar") | |
| with gr.Column(scale=1): | |
| image_output = gr.Image(label="Imagen asociada") | |
| def process_input(message, history): | |
| full_response, image = chat_function(message, history) | |
| history.append((message, full_response)) | |
| return history, history, image | |
| submit_button.click(process_input, inputs=[chatbot_input, chatbot_history], outputs=[chatbot_output, chatbot_history, image_url]) | |
| image_url.change(fn=update_image, inputs=image_url, outputs=image_output) | |
| # Launch the interface | |
| demo.launch(debug=True) | |