import gradio as gr from huggingface_hub import InferenceClient theme = gr.themes.Monochrome( primary_hue=gr.themes.Color(c100="#f5f5f3", c200="#fbfaf8", c300="#f9f9f9", c400="#eee9ee", c50="rgba(255, 255, 255, 1)", c500="rgba(0, 0, 0, 1)", c600="rgba(26.934374999999992, 26.934374999999992, 26.934374999999992, 1)", c700="rgba(10.943750000000012, 10.943750000000012, 10.943750000000012, 1)", c800="rgba(17.053125000000005, 17.053125000000005, 17.053125000000005, 1)", c900="#fffefe", c950="#fffefe"), secondary_hue=gr.themes.Color(c100="#f29c74", c200="#f4b7a8", c300="#fffefe", c400="#fffefe", c50="#e46e45", c500="#fffefe", c600="#fffefe", c700="#fffefe", c800="#fffefe", c900="#fffefe", c950="#fffefe"), neutral_hue=gr.themes.Color(c100="#6b8ea8", c200="#a4c5e8", c300="rgba(0, 0, 0, 1)", c400="rgba(0, 0, 0, 1)", c50="#284566", c500="rgba(0, 0, 0, 1)", c600="rgba(0, 0, 0, 1)", c700="rgba(0, 0, 0, 1)", c800="rgba(0, 0, 0, 1)", c900="rgba(0, 0, 0, 1)", c950="rgba(0, 0, 0, 1)"), font=[gr.themes.GoogleFont('open sans'), 'ui-sans-serif', 'system-ui', 'sans-serif'], ).set( body_background_fill='*primary_50', body_background_fill_dark='*primary_700', body_text_color='*primary_500', body_text_color_dark='*primary_400', body_text_color_subdued='*neutral_50', body_text_color_subdued_dark='*primary_300', background_fill_primary='*primary_400', background_fill_primary_dark='*neutral_100', background_fill_secondary='*primary_400', background_fill_secondary_dark='*neutral_50', border_color_accent_dark='*secondary_50', border_color_accent_subdued_dark='*primary_50', border_color_primary='*neutral_50', border_color_primary_dark='*neutral_100', color_accent='*primary_50', color_accent_soft_dark='*neutral_100', block_background_fill_dark='*neutral_50', checkbox_background_color='*primary_400', checkbox_background_color_selected='*primary_700', checkbox_background_color_selected_dark='*neutral_50', checkbox_label_border_color_dark='*neutral_50', input_background_fill='*primary_50', input_background_fill_dark='*neutral_100', input_border_color='*neutral_50', input_border_color_dark='*neutral_50', input_border_width='2px', table_odd_background_fill='*neutral_100', button_primary_background_fill='*neutral_50', button_primary_background_fill_dark='*neutral_100', button_primary_background_fill_hover='*neutral_100' ) #STEP 1 FROM SEMATIC SEARCH from sentence_transformers import SentenceTransformer import torch #STEP 2 FROM SEMATIC SEARCH # Open the weather.txt file in read mode with UTF-8 encoding with open("weather.txt", "r", encoding="utf-8") as file: # Read the entire contents of the file and store it in a variable weather_text = file.read() with open("luggage.txt", "r", encoding="utf-8") as file: # Read the entire contents of the file and store it in a variable luggage_text = file.read() with open("attractions.txt", "r", encoding="utf-8") as file: # Read the entire contents of the file and store it in a variable attraction_text = file.read() with open("food.txt", "r", encoding="utf-8") as file: # Read the entire contents of the file and store it in a variable food_text = file.read() #STEP 3 FROM SEMATIC SEARCH def preprocess_text(text): # Strip extra whitespace from the beginning and the end of the text cleaned_text = text.strip() # Split the cleaned_text by every newline character (\n) chunks = cleaned_text.split("***") # Create an empty list to store cleaned chunks cleaned_chunks = [] # Write your for-in loop below to clean each chunk and add it to the cleaned_chunks list for chunk in chunks: chunk.strip() if chunk != "": cleaned_chunks.append(chunk) return cleaned_chunks # Call the preprocess_text function and store the result in a cleaned_chunks variable cleaned_chunks_weather = preprocess_text(weather_text) # Complete this line cleaned_chunks_luggage = preprocess_text(luggage_text) cleaned_chunks_attraction = preprocess_text(attraction_text) cleaned_chunks_food = preprocess_text(food_text) #STEP 4 FROM SEMATIC SEARCH # Load the pre-trained embedding model that converts text to vectors model = SentenceTransformer('all-MiniLM-L6-v2') def create_embeddings(text_chunks): # Convert each text chunk into a vector embedding and store as a tensor chunk_embeddings = model.encode(text_chunks, convert_to_tensor=True) # Replace ... with the text_chunks list return chunk_embeddings # Call the create_embeddings function and store the result in a new chunk_embeddings variable chunk_embeddings_weather = create_embeddings(cleaned_chunks_weather) # Complete this line chunk_embeddings_luggage = create_embeddings(cleaned_chunks_luggage) chunk_embeddings_attraction = create_embeddings(cleaned_chunks_attraction) chunk_embeddings_food = create_embeddings(cleaned_chunks_food) #STEP 5 FROM SEMATIC SEARCH # Define a function to find the most relevant text chunks for a given query, chunk_embeddings, and text_chunks def get_top_chunks(query, chunk_embeddings, text_chunks): # Convert the query text into a vector embedding query_embedding = model.encode(query, convert_to_tensor = True) # Complete this line # Normalize the query embedding to unit length for accurate similarity comparison query_embedding_normalized = query_embedding / query_embedding.norm() # Normalize all chunk embeddings to unit length for consistent comparison chunk_embeddings_normalized = chunk_embeddings / chunk_embeddings.norm(dim=1, keepdim=True) # Calculate cosine similarity between query and all chunks using matrix multiplication similarities = torch.matmul(chunk_embeddings_normalized, query_embedding_normalized) # Complete this line # Print the similarities #print(similarities) # Find the indices of the 3 chunks with highest similarity scores top_indices = torch.topk(similarities, k=3).indices # Print the top indices #print(top_indices) # Create an empty list to store the most relevant chunks top_chunks = [] # Loop through the top indices and retrieve the corresponding text chunks for top_index in top_indices: top_chunks.append(text_chunks[top_index]) # Return the list of most relevant chunks return top_chunks #STEP 6 FROM SEMANTIC SEARCH client = InferenceClient("Qwen/Qwen2.5-72B-Instruct") def respond(message, history, language, chatbot_mode, destinations, trip_length, trip_unit, trip_season, luggage_types, luggage_size, food_prefs, activity): destinations = destinations or [] trip_length = trip_length or "Not specified" trip_unit = trip_unit or "" trip_season = trip_season or "Not specified" luggage_types = luggage_types or [] luggage_size = luggage_size or "Not specified" food_prefs = food_prefs or [] activity = activity or [] language = language or "English" #conduct a semantic search for each of our files top_weather = get_top_chunks(message, chunk_embeddings_weather, cleaned_chunks_weather) top_luggage = get_top_chunks(message, chunk_embeddings_luggage, cleaned_chunks_luggage) top_attraction = get_top_chunks(message, chunk_embeddings_attraction, cleaned_chunks_attraction) top_food = get_top_chunks(message, chunk_embeddings_food, cleaned_chunks_food) str_top_weather = "\n".join(top_weather) str_top_luggage = "\n".join(top_luggage) str_top_attraction = "\n".join(top_attraction) str_top_food = "\n".join(top_food) #collect inputed data from the sidebar elements ctx = ( f"Language: {language}" f"Destination: {', '.join(destinations) if destinations else 'Not specified'}\n" f"Trip Length: {trip_length} {trip_unit}\n" f"Trip Season: {trip_season}\n" f"Luggage: {', '.join(luggage_types) if luggage_types else 'Not specified'}, " f"Size: {luggage_size}L\n" f"Food Preferences: {', '.join(food_prefs) if food_prefs else 'Not specified'}\n" f"Activity Preferences: {', '.join(activity) if activity else 'Not specified'}\n" ) if chatbot_mode == "Packing": messages = [{ "role": "system", "content": ( f"You are a friendly and Gen Z travel chatbot helping with packing advice.\n\n" f"{ctx}\n" f"Relevant context:\n{str_top_weather}\n{str_top_luggage}" f"Please respond in {language}" ) }] elif chatbot_mode == "Food/Attractions": messages = [{ "role": "system", "content": ( f"You are a friendly and Gen Z travel chatbot recommending food and attractions.\n\n" f"{ctx}\n" f"Relevant context:\n{str_top_food}\n{str_top_attraction}" f"Please respond in {language}" ) }] else: messages = [{ "role": "system", "content": ( f"You are a friendly and Gen Z travel chatbot helping travelers plan trips to San Francisco and/or Los Angeles.\n\n" f"{ctx}\n" f"Use relevant context:\n{str_top_weather}\n{str_top_luggage}\n{str_top_food}\n{str_top_attraction}" f"Please respond in {language}" ) }] if history: for user_msg, bot_reply in history: messages.append({"role": "user", "content": user_msg}) messages.append({"role": "assistant", "content": bot_reply}) messages.append({"role": "user", "content": message}) response = client.chat_completion( messages, max_tokens = 2000, temperature = 1 ) if isinstance(response, dict): reply = response['choices'][0]['message']['content'].strip() else: reply = response.strip() history.append((message, reply)) return history, "" def reset_inputs(): return ( None, # language None, # chatbot_mode [], # destinations None, # trip_length None, # trip_unit None, # trip_season [], # luggage_types 20, # luggage_size (default) [], # food_prefs [] # activity ) def update_visibility(chatbot_mode): if chatbot_mode == "Packing": return gr.update(visible=True), gr.update(visible=False) elif chatbot_mode == "Food/Attractions": return gr.update(visible=False), gr.update(visible=True) else: return gr.update(visible=True), gr.update(visible=True) with gr.Blocks(theme=theme) as demo: with gr.Row(): # ─── left column: your controls ─── with gr.Column(scale=1): gr.Markdown("### Chatbot Settings") language = gr.Radio( choices=["English","Español", "Italiano", "Français", "日本語", "中文"], label="What language would you like to use?" ) chatbot_mode = gr.Radio( choices=["Packing", "Food/Attractions"], label="What do you need help with?" ) gr.Markdown("### Destination") destinations = gr.CheckboxGroup( choices=["San Francisco","Los Angeles"], label="Where are you going?" ) gr.Markdown("### Trip Length") trip_length = gr.Number(label="How long is your trip?", precision=0) trip_unit = gr.Radio( choices=["day", "week", "month"], label="Trip length unit:" ) trip_season = gr.Radio( choices=["Warm/Dry (May to October)", "Cool/Wet (November to April)"], label="Season:" ) with gr.Group(visible=True) as packing_group: #gr.Markdown("### Luggage") luggage_types = gr.CheckboxGroup( choices=["Carry-on", "Checked"], label="What is your luggage type?" ) luggage_size = gr.Slider( minimum=10, maximum=100, step=10, value=20, label="What is the size of your luggage (liters)?" ) with gr.Group(visible=True) as food_group: #gr.Markdown("### Food") food_prefs = gr.Dropdown( choices=["Italian", "Thai", "Mexican", "Japanese", "Vegan", "Seafood"], multiselect=True, label="What are your food preferences?" ) #gr.Markdown("### Activities") activity = gr.Dropdown( choices=["Outdoor & Nature", "Indoor", "Museums", "Shopping", "Relaxation"], multiselect=True, label="What are your activity preferences?" ) chatbot_mode.change( fn=update_visibility, inputs=[chatbot_mode], outputs=[packing_group, food_group] ) reset_btn = gr.Button("Reset All", variant="secondary") reset_btn.click( fn=reset_inputs, inputs=[], outputs=[ language, chatbot_mode, destinations, trip_length, trip_unit, trip_season, luggage_types, luggage_size, food_prefs, activity ] ) with gr.Column(scale=3): gr.Image(value="Go Buddy2.png", interactive=False, show_label=False) # 1) the chat history panel chat_box = gr.Chatbot(height=900) # 2) where the user types msg = gr.Textbox( placeholder="Ask me anything…", lines=1, label = "How can we help you?" ) # 3) send button send_btn = gr.Button("Send") # 4) hook up the click event send_btn.click( fn=respond, inputs=[ msg, chat_box, language, chatbot_mode, destinations, trip_length, trip_unit, trip_season, luggage_types, luggage_size, food_prefs, activity ], outputs=[chat_box, msg] ) msg.submit( fn=respond, inputs=[ msg, chat_box, language, chatbot_mode, destinations, trip_length, trip_unit, trip_season, luggage_types, luggage_size, food_prefs, activity ], outputs=[chat_box, msg] ) demo.launch(debug=True)