Update app.py

app.py

@@ -169,177 +169,4 @@ with gr.Blocks(theme=theme) as demo:
     
 
 # Launch the Gradio app to allow user interaction
-demo.launch(share=True)
-
-# import gradio as gr
-# from sentence_transformers import SentenceTransformer, util
-# import openai
-# import os
-
-# os.environ["TOKENIZERS_PARALLELISM"] = "false"
-
-# # Initialize paths and model identifiers for easy configuration and maintenance
-# filename = "output_topic_details.txt"  # Path to the file storing chess-specific details
-# retrieval_model_name = 'output/sentence-transformer-finetuned/'
-
-# openai.api_key = os.environ["OPENAI_API_KEY"]
-
-# system_message = "You are a eco-friendly travel chatbot specialized in providing information on eco-friendly restaurants, hotels, and attractions in NYC."
-# # Initial system message to set the behavior of the assistant
-# messages = [{"role": "system", "content": system_message}]
-
-# # Attempt to load the necessary models and provide feedback on success or failure
-# try:
-#     retrieval_model = SentenceTransformer(retrieval_model_name)
-#     print("Models loaded successfully.")
-# except Exception as e:
-#     print(f"Failed to load models: {e}")
-
-# def load_and_preprocess_text(filename):
-#     """
-#     Load and preprocess text from a file, removing empty lines and stripping whitespace.
-#     """
-#     try:
-#         with open(filename, 'r', encoding='utf-8') as file:
-#             segments = [line.strip() for line in file if line.strip()]
-#         print("Text loaded and preprocessed successfully.")
-#         return segments
-#     except Exception as e:
-#         print(f"Failed to load or preprocess text: {e}")
-#         return []
-
-# segments = load_and_preprocess_text(filename)
-
-# def find_relevant_segment(user_query, segments):
-#     """
-#     Find the most relevant text segment for a user's query using cosine similarity among sentence embeddings.
-#     This version finds the best match based on the content of the query.
-#     """
-#     try:
-#         # Lowercase the query for better matching
-#         lower_query = user_query.lower()
-        
-#         # Encode the query and the segments
-#         query_embedding = retrieval_model.encode(lower_query)
-#         segment_embeddings = retrieval_model.encode(segments)
-        
-#         # Compute cosine similarities between the query and the segments
-#         similarities = util.pytorch_cos_sim(query_embedding, segment_embeddings)[0]
-        
-#         # Find the index of the most similar segment
-#         best_idx = similarities.argmax()
-        
-#         # Return the most relevant segment
-#         return segments[best_idx]
-#     except Exception as e:
-#         print(f"Error in finding relevant segment: {e}")
-#         return ""
-
-# def generate_response(user_query, relevant_segment):
-#     """
-#     Generate a response emphasizing the bot's capability in providing eco-friendly travel information.
-#     """
-#     try:
-#         user_message = f"Here's the information on eco-friendly travel information: {relevant_segment}"
-
-#         # Append user's message to messages list
-#         messages.append({"role": "user", "content": user_message})
-        
-#         response = openai.ChatCompletion.create(
-#             model="gpt-3.5-turbo",
-#             messages=messages,
-#             max_tokens=150,
-#             temperature=0.2,
-#             top_p=1,
-#             frequency_penalty=0,
-#             presence_penalty=0
-#         )
-        
-#         # Extract the response text
-#         output_text = response['choices'][0]['message']['content'].strip()
-        
-#         # Append assistant's message to messages list for context
-#         messages.append({"role": "assistant", "content": output_text})
-        
-#         return output_text
-        
-#     except Exception as e:
-#         print(f"Error in generating response: {e}")
-#         return f"Error in generating response: {e}"
-
-# def query_model(question):
-#     """
-#     Process a question, find relevant information, and generate a response.
-#     """
-#     if question == "":
-#         return "Welcome to GreenGuide! Ask me anything about eco-friendly hotels, restaurants, and things to do in NYC."
-#     relevant_segment = find_relevant_segment(question, segments)
-#     if not relevant_segment:
-#         return "Could not find specific information. Please refine your question."
-#     response = generate_response(question, relevant_segment)
-#     return response
-
-# # Define the HTML iframe content
-
-# iframe = '''
-# <iframe src="https://www.google.com/maps/embed?pb=!1m18!1m12!1m3!1d193595.2528001417!2d-74.1444872802558!3d40.69763123330436!2m3!1f0!2f0!3f0!3m2!1i1024!2i768!4f13.1!3m3!1m2!1s0x89c24fa5d33f083b%3A0xc80b8f06e177fe62!2sNew%20York%2C%20NY!5e0!3m2!1sen!2sus!4v1722483445443!5m2!1sen!2sus" width="600" height="450" style="border:0;" allowfullscreen="" loading="lazy" referrerpolicy="no-referrer-when-downgrade"></iframe>
-# '''
-
-
-# # Define the welcome message and specific topics the chatbot can provide information about
-# welcome_message = """
-# # 🌱 Welcome to GreenGuide!
-# ## Your AI-driven assistant for all eco-friendly travel-related queries in NYC. Created by Eva, Amy, and Ambur of the 2024 Kode With Klossy NYC AI/ML Camp. 
-# """
-
-# topics = """
-# ### Feel free to ask me anything things to do in the city!
-# - Hotels (affordable, luxury)
-# - Restaurants (regular, vegetarian, vegan)
-# - Parks & Gardens
-# - Thrift Stores
-# - Attractions
-# """
-
-# # Create a Gradio HTML component
-# def display_iframe():
-#     return iframe
-# def display_image():
-#     return "https://i.giphy.com/media/v1.Y2lkPTc5MGI3NjExZzdqMnkzcWpjbGhmM3hzcXp0MGpuaTF5djR4bjBxM3Biam5zbzNnMCZlcD12MV9pbnRlcm5hbF9naWZfYnlfaWQmY3Q9cw/GxMnTi3hV3qaIgbgQL/giphy.gif"
-#     #return "https://cdn-uploads.huggingface.co/production/uploads/6668622b72b61ba78fe7d4bb/PkWjNxvGm9MOqGkZdiT4e.png"
-# theme = gr.themes.Monochrome(
-#     primary_hue="amber", #okay this did NOT work lmaoo
-#     secondary_hue="rose",
-# ).set(
-#     background_fill_primary='#CBE9A2',  # BACKGROUND
-#     background_fill_primary_dark='#768550',
-#     background_fill_secondary='#768550',  # BUTTON HOVER
-#     background_fill_secondary_dark='#99a381', #LOADING BAR
-#     border_color_accent='#768550',
-#     border_color_accent_dark='#768550',
-#     border_color_accent_subdued='#768550',
-#     border_color_primary='#03a9f4',
-#     block_border_color='#b3e5fc',
-#     button_primary_background_fill='#768550',
-#     button_primary_background_fill_dark='#768550'
-# )
-
-# # Setup the Gradio Blocks interface with custom layout components
-# with gr.Blocks(theme=theme) as demo:
-#     gr.Image("header2.png", show_label = False, show_share_button = False, show_download_button = False) #CHANGE !!
-#     gr.Markdown(welcome_message)  # Display the formatted welcome message
-#     with gr.Row():
-#         with gr.Column():
-#             gr.Markdown(topics)  # Show the topics on the left side
-#     with gr.Row():
-#         with gr.Column():
-#             question = gr.Textbox(label="Your question", placeholder="What do you want to ask about?")
-#             answer = gr.Textbox(label="GreenGuide Response", placeholder="GreenGuide will respond here...", interactive=False, lines=10)
-#             submit_button = gr.Button("Submit")
-#             submit_button.click(fn=query_model, inputs=question, outputs=answer)
-
-#     gr.HTML(iframe)        
-    
-
-# # Launch the Gradio app to allow user interaction
-# demo.launch(share=True)
+demo.launch(share=True)