app.py

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
import gradio as gr

# Load Llama 3.2 model
model_name = "meta-llama/Llama-3.2-3B-Instruct"  # Replace with the exact model path
tokenizer = AutoTokenizer.from_pretrained(model_name)
#model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.float16)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map=None, torch_dtype=torch.float32)

# Helper function to process long contexts
MAX_TOKENS = 100000  # Replace with the max token limit of the Llama model


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import faiss
import torch
import pandas as pd
from sentence_transformers import SentenceTransformer
from transformers import AutoTokenizer, AutoModelForCausalLM
import gradio as gr

# Load Llama model
#model_name = "meta-llama/Llama-3.2-3B-Instruct"  # Replace with the exact model path
#tokenizer = AutoTokenizer.from_pretrained(model_name)
#model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torch_dtype=torch.float16)

# Load Sentence Transformer model for embeddings
embedder = SentenceTransformer('distiluse-base-multilingual-cased')  # Suitable for German text

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# Load the CSV data
url = 'https://www.bofrost.de/datafeed/DE/products.csv'
data = pd.read_csv(url, sep='|')

# List of columns to keep
columns_to_keep = [
    'ID', 'Name', 'Description', 'Price', 
    'ProductCategory', 'Grammage', 
    'BasePriceText', 'Rating', 'RatingCount',
    'Ingredients', 'CreationDate', 'Keywords', 'Brand'
]

# Filter the DataFrame
data_cleaned = data[columns_to_keep]

# Remove unwanted characters from the 'Description' column
data_cleaned['Description'] = data_cleaned['Description'].str.replace(r'[^\w\s.,;:\'"/?!€$%&()\[\]{}<>|=+\\-]', ' ', regex=True)

# Combine relevant text columns for embedding
data_cleaned['combined_text'] = data_cleaned.apply(lambda row: ' '.join([str(row[col]) for col in ['Name', 'Description', 'Keywords'] if pd.notnull(row[col])]), axis=1)

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# Generate embeddings for the combined text
embeddings = embedder.encode(data_cleaned['combined_text'].tolist(), convert_to_tensor=True)

# Convert embeddings to numpy array
embeddings = embeddings.cpu().detach().numpy()

# Initialize FAISS index
d = embeddings.shape[1]  # Dimension of embeddings
faiss_index = faiss.IndexFlatL2(d)

# Add embeddings to the index
faiss_index.add(embeddings)

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def search_products(query, top_k=7):
    # Generate embedding for the query
    query_embedding = embedder.encode([query], convert_to_tensor=True).cpu().detach().numpy()

    # Search FAISS index
    distances, indices = faiss_index.search(query_embedding, top_k)

    # Retrieve corresponding products
    results = data_cleaned.iloc[indices[0]].to_dict(orient='records')
    return results



# Update the prompt construction to include ChromaDB results
def construct_system_prompt( context):
    prompt = f"You are a friendly bot specializing in Bofrost products. Return comprehensive german answers. Always add product ids. Use the following product descriptions:\n\n{context}\n\n"
    return prompt

# Helper function to construct the prompt
def construct_prompt(user_input, context, chat_history, max_history_turns=1): # Added max_history_turns
    system_message = construct_system_prompt(context)
    prompt = f"<|begin_of_text|><|start_header_id|>system<|end_header_id|>\n\n{system_message}<|eot_id|>"
    
    # Limit history to the last max_history_turns
    for i, (user_msg, assistant_msg) in enumerate(chat_history[-max_history_turns:]): 
        prompt += f"<|start_header_id|>user<|end_header_id|>\n\n{user_msg}<|eot_id|>"
        prompt += f"<|start_header_id|>assistant<|end_header_id|>\n\n{assistant_msg}<|eot_id|>"
    
    prompt += f"<|start_header_id|>user<|end_header_id|>\n\n{user_input}<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n"
    print("-------------------------")
    print(prompt)
    return prompt

def chat_with_model(user_input, chat_history=[]):
    # Search for relevant products
    search_results = search_products(user_input)

    # Create context with search results
    if search_results:
        context = "Product Context:\n"
        for product in search_results:
            context += f"Produkt ID: {product['ID']}\n"
            context += f"Name: {product['Name']}\n"
            context += f"Beschreibung: {product['Description']}\n"
            context += f"Preis: {product['Price']}€\n"
            context += f"Bewertung: {product['Rating']} ({product['RatingCount']} Bewertungen)\n"
            context += f"Kategorie: {product['ProductCategory']}\n"
            context += f"Marke: {product['Brand']}\n"
            context += "---\n"
    else:
        context = "Das weiß ich nicht."
    print("context: ------------------------------------- \n"+context)
    # Pass both user_input and context to construct_prompt
    prompt = construct_prompt(user_input, context, chat_history) # This line is changed
    print("prompt: ------------------------------------- \n"+prompt)
    input_ids = tokenizer.encode(prompt, return_tensors="pt", truncation=True, max_length=4096).to("cpu")
    tokenizer.pad_token = tokenizer.eos_token
    attention_mask = torch.ones_like(input_ids).to("cpu")
    outputs = model.generate(input_ids, attention_mask=attention_mask,
                             max_new_tokens=1200, do_sample=True, 
                             top_k=50, temperature=0.7)
    response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
    print("respone: ------------------------------------- \n"+response)
    chat_history.append((context, response))  # or chat_history.append((user_input, response)) if you want to store user input
    return response, chat_history

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# Gradio Interface
def gradio_interface(user_input, history):
    response, updated_history = chat_with_model(user_input, history)
    return response, updated_history

with gr.Blocks() as demo:
    gr.Markdown("# 🦙 Llama Instruct Chat with ChromaDB Integration")
    with gr.Row():
        user_input = gr.Textbox(label="Your Message", lines=2, placeholder="Type your message here...")
        submit_btn = gr.Button("Send")
    chat_history = gr.State([])
    chat_display = gr.Textbox(label="Chat Response", lines=10, placeholder="Chat history will appear here...", interactive=False)
    submit_btn.click(gradio_interface, inputs=[user_input, chat_history], outputs=[chat_display, chat_history])

demo.launch(debug=True)