Update app.py

app.py

@@ -9,3 +9,152 @@ model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto", torc
 
 # Helper function to process long contexts
 MAX_TOKENS = 100000  # Replace with the max token limit of the Llama model
+
+
+#########
+###
+#########
+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
+
+########
+###
+###
+#####
+# 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)
+
+######
+##
+#####
+
+# 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)
+
+#######
+##
+######
+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("cuda")
+    tokenizer.pad_token = tokenizer.eos_token
+    attention_mask = torch.ones_like(input_ids).to("cuda")
+    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
+
+#####
+###
+###
+# 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)