Fix Folders, Add readme

recipes/quickstart/Multi-Modal-RAG/gradio_app.py

@@ -1,237 +0,0 @@
-import gradio as gr
-import pandas as pd
-import lancedb
-from lancedb.pydantic import LanceModel, Vector
-from lancedb.embeddings import get_registry
-from pathlib import Path
-from PIL import Image
-import io
-import base64
-from together import Together
-import os
-import logging
-import argparse
-import numpy as np
-
-
-# Set up argument parsing
-parser = argparse.ArgumentParser(description="Interactive Fashion Assistant")
-parser.add_argument("--images_folder", required=True, help="Path to the folder containing compressed images")
-parser.add_argument("--csv_path", required=True, help="Path to the CSV file with clothing data")
-parser.add_argument("--table_path", default="~/.lancedb", help="Table path for LanceDB")
-parser.add_argument("--use_existing_table", action="store_true", help="Use existing table if it exists")
-parser.add_argument("--api_key", required=True, help="Together API key")
-args = parser.parse_args()
-
-# Set up logging
-logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
-
-print("Starting the Fashion Assistant application...")
-
-# Set up the sentence transformer model for CPU use
-print("Initializing the sentence transformer model...")
-model = get_registry().get("sentence-transformers").create(name="BAAI/bge-large-en-v1.5", device="cpu")
-print("Sentence transformer model initialized successfully.")
-
-# Define the schema for our LanceDB table
-class Schema(LanceModel):
-    Filename: str
-    Title: str
-    Size: str
-    Gender: str
-    Description: str = model.SourceField()
-    Category: str
-    Type: str
-    vector: Vector(model.ndims()) = model.VectorField()
-
-# Connect to LanceDB and create/load the table
-print("Connecting to LanceDB...")
-db = lancedb.connect(args.table_path)
-if args.use_existing_table:
-    tbl = db.open_table("clothes")
-else:
-    tbl = db.create_table(name="clothes", schema=Schema, mode="overwrite")
-    # Load and clean data
-    print(f"Loading and cleaning data from CSV: {args.csv_path}")
-    df = pd.read_csv(args.csv_path)
-    df = df.dropna().astype(str)
-    tbl.add(df.to_dict('records'))
-    print(f"Loaded and cleaned {len(df)} records into the LanceDB table.")
-
-print("Connected to LanceDB and created/loaded the 'clothes' table.")
-
-
-
-# Set up the Together API client
-os.environ["TOGETHER_API_KEY"] = args.api_key
-client = Together(api_key=args.api_key)
-print("Together API client set up successfully.")
-
-def encode_image(image):
-    buffered = io.BytesIO()
-    image.save(buffered, format="JPEG")
-    return base64.b64encode(buffered.getvalue()).decode('utf-8')
-
-def generate_description(image):
-    print("Generating description for uploaded image...")
-    base64_image = encode_image(image)
-    try:
-        response = client.chat.completions.create(
-            model="meta-llama/Llama-Vision-Free",
-            messages=[
-                {
-                    "role": "user",
-                    "content": [
-                        {
-                            "type": "image_url",
-                            "image_url": {
-                                "url": f"data:image/jpeg;base64,{base64_image}"
-                            }
-                        },
-                        {
-                            "type": "text",
-                            "text": "Describe this clothing item in detail."
-                        }
-                    ]
-                }
-            ],
-            max_tokens=512,
-            temperature=0.7,
-        )
-        description = response.choices[0].message.content
-        print(f"Generated description: {description}")
-        return description
-    except Exception as e:
-        print(f"Error generating description: {e}")
-        return "Error generating description"
-
-def process_chat_input(chat_history, user_input):
-    print(f"Processing chat input: {user_input}")
-    messages = [
-        {"role": "system", "content": "You are a helpful fashion assistant."}
-    ]
-    for user_msg, assistant_msg in chat_history:
-        messages.append({"role": "user", "content": user_msg})
-        messages.append({"role": "assistant", "content": assistant_msg})
-    
-    user_input += ". START YOUR MESSAGE DIRECTLY WITH A RESPONSE LIST. DO NOT REPEAT THE NAME OF THE ITEM MENTIONED IN THE QUERY."
-    messages.append({"role": "user", "content": user_input})
-    print(f"Chat history: {messages}")
-    try:
-        bot_response = client.chat.completions.create(
-            model="meta-llama/Llama-Vision-Free",
-            messages=messages,
-            max_tokens=512,
-            temperature=0.7,
-        ).choices[0].message.content
-        
-        print(f"Bot response: {bot_response}")
-        return user_input, bot_response
-    except Exception as e:
-        print(f"Error processing chat input: {e}")
-        return user_input, "Error processing chat input"
-
-def retrieve_similar_items(description, n=10):
-    print(f"Retrieving similar items for: {description}")
-    try:
-        results = tbl.search(description).limit(n).to_pandas()
-        print(f"Retrieved {len(results)} similar items.")
-        return results
-    except Exception as e:
-        print(f"Error retrieving similar items: {e}")
-        return pd.DataFrame()
-
-def rewrite_query(original_query, item_description):
-    print(f"Rewriting query: {original_query}")
-    messages = [
-        {"role": "system", "content": "You are a helpful fashion assistant. Rewrite the user's query to include details from the item description."},
-        {"role": "user", "content": f"Item description: {item_description}"},
-        {"role": "user", "content": f"User query: {original_query}"},
-        {"role": "user", "content": "Please rewrite the query to include relevant details from the item description."}
-    ]
-    
-    try:
-        response = client.chat.completions.create(
-            model="meta-llama/Llama-Vision-Free",
-            messages=messages,
-            max_tokens=512,
-            temperature=0.7,
-        )
-        rewritten_query = response.choices[0].message.content
-        print(f"Rewritten query: {rewritten_query}")
-        return rewritten_query
-    except Exception as e:
-        print(f"Error rewriting query: {e}")
-        return original_query
-
-def fashion_assistant(image, chat_input, chat_history):
-    if chat_input is not "":
-        print("Processing chat input...")
-        last_description = chat_history[-1][1] if chat_history else ""
-        #rewritten_query = rewrite_query(chat_input, last_description)
-        user_message, bot_response = process_chat_input(chat_history, chat_input)
-        similar_items = retrieve_similar_items(bot_response)
-        gallery_data = create_gallery_data(similar_items)
-        return chat_history + [[user_message, bot_response]], bot_response, gallery_data, last_description
-    elif image is not None:
-        print("Processing uploaded image...")
-        description = generate_description(image)
-        user_message = f"I've uploaded an image. The description is: {description}"
-        user_message, bot_response = process_chat_input(chat_history, user_message)
-        similar_items = retrieve_similar_items(description)
-        gallery_data = create_gallery_data(similar_items)
-        return chat_history + [[user_message, bot_response]], bot_response, gallery_data, description
-    else:
-        print("No input provided.")
-        return chat_history, "", [], ""
-
-def create_gallery_data(results):
-    return [
-        (str(Path(args.images_folder) / row['Filename']), f"{row['Title']}\n{row['Description']}")
-        for _, row in results.iterrows()
-    ]
-
-def on_select(evt: gr.SelectData):
-    return f"Selected {evt.value} at index {evt.index}"
-
-def update_chat(image, chat_input, chat_history, last_description):
-    new_chat_history, last_response, gallery_data, new_description = fashion_assistant(image, chat_input, chat_history)
-    if new_description:
-        last_description = new_description
-    return new_chat_history, new_chat_history, "", last_response, gallery_data, last_description
-
-# Define the Gradio interface
-print("Setting up Gradio interface...")
-with gr.Blocks() as demo:
-    gr.Markdown("# Interactive Fashion Assistant")
-    with gr.Row():
-        with gr.Column(scale=1):
-            image_input = gr.Image(type="pil", label="Upload Clothing Image")
-        with gr.Column(scale=1):
-            chatbot = gr.Chatbot(label="Chat History")
-            chat_input = gr.Textbox(label="Chat Input")
-            chat_button = gr.Button("Send")
-        with gr.Column(scale=2):
-            gallery = gr.Gallery(
-                label="Retrieved Clothes",
-                show_label=True,
-                elem_id="gallery",
-                columns=[5],
-                rows=[2],
-                object_fit="contain",
-                height="auto"
-            )
-            selected_image = gr.Textbox(label="Selected Image")
-    
-    chat_state = gr.State([])
-    last_description = gr.State("")
-    
-    image_input.change(update_chat, inputs=[image_input, chat_input, chat_state, last_description], 
-                       outputs=[chat_state, chatbot, chat_input, chat_input, gallery, last_description])
-    chat_button.click(update_chat, inputs=[image_input, chat_input, chat_state, last_description], 
-                      outputs=[chat_state, chatbot, chat_input, chat_input, gallery, last_description])
-    gallery.select(on_select, None, selected_image)
-
-print("Gradio interface set up successfully. Launching the app...")
-demo.launch()
-print("Fashion Assistant application is now running!")