Update app.py

app.py

@@ -106,10 +106,16 @@ def query_model(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!
-
+# 🌱 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. 
 """
 
@@ -120,33 +126,34 @@ topics = """
 - Parks & Gardens
 - Thrift Stores
 - Attractions
-
 """
 
 # Create a Gradio HTML component
 def display_iframe():
     return iframe
 def display_image():
-    return "https://cdn-uploads.huggingface.co/production/uploads/6668622b72b61ba78fe7d4bb/PkWjNxvGm9MOqGkZdiT4e.png"
+    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",
+    primary_hue="amber", #okay this did NOT work lmaoo
     secondary_hue="rose",
 ).set(
-    background_fill_primary='*primary_200',
-    background_fill_primary_dark='*primary_200',
-    background_fill_secondary='*secondary_300',
-    background_fill_secondary_dark='*secondary_300',
-    border_color_accent='*secondary_200',
-    border_color_accent_dark='*secondary_600',
-    border_color_accent_subdued='*secondary_200',
-    border_color_primary='*secondary_300',
-    block_border_color='*secondary_200',
-    button_primary_background_fill='*secondary_300',
-    button_primary_background_fill_dark='*secondary_300'
+    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='JohnSmith9982/small_and_pretty') as demo:
+with gr.Blocks(theme=theme) as demo:
+    gr.Image("chess-pieces.jpeg", 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():
@@ -157,7 +164,173 @@ with gr.Blocks(theme='JohnSmith9982/small_and_pretty') as demo:
             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)
+
+# 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 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
+# - Restaurants
+# - Parks & Gardens
+# - Thrift Stores
+# - Attractions
+
+# """
+
+# # Create a Gradio HTML component
+# def display_iframe():
+#     return iframe
+# def display_image():
+#     return "https://cdn-uploads.huggingface.co/production/uploads/6668622b72b61ba78fe7d4bb/PkWjNxvGm9MOqGkZdiT4e.png"
+# theme = gr.themes.Monochrome(
+#     primary_hue="amber",
+#     secondary_hue="rose",
+# ).set(
+#     background_fill_primary='*primary_200',
+#     background_fill_primary_dark='*primary_200',
+#     background_fill_secondary='*secondary_300',
+#     background_fill_secondary_dark='*secondary_300',
+#     border_color_accent='*secondary_200',
+#     border_color_accent_dark='*secondary_600',
+#     border_color_accent_subdued='*secondary_200',
+#     border_color_primary='*secondary_300',
+#     block_border_color='*secondary_200',
+#     button_primary_background_fill='*secondary_300',
+#     button_primary_background_fill_dark='*secondary_300'
+# )
+
+# # Setup the Gradio Blocks interface with custom layout components
+# with gr.Blocks(theme='JohnSmith9982/small_and_pretty') as demo:
+#     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)
+    
+
+# # Launch the Gradio app to allow user interaction
+# demo.launch(share=True)