app.py

import gradio as gr
from sentence_transformers import SentenceTransformer, util
import transformers
from transformers import pipeline
import webbrowser
import openai
import os

os.environ["TOKENIZERS_PARALLELISM"] = "false"

# Initialize paths and model identifiers for easy configuration and maintenance
filename = "output_composting_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"]

# 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 to provide information related to composting food.
    """
    
    try:
        system_message = "You are a chatbot specialized in providing information about food composting tips, tricks, and basics."
        user_message = f"Here's the information on composting: {relevant_segment}"
        messages = [
            {"role": "system", "content": system_message},
            {"role": "user", "content": user_message}
        ]
        response = openai.ChatCompletion.create(
            model="gpt-4o",
            messages=messages,
            max_tokens=200,
            temperature=0.5,
            top_p=1,
            frequency_penalty=0.5,
            presence_penalty=0.5
        )
        return response['choices'][0]['message']['content'].strip()
    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 CompBot! Ask me anything about composting tips, tricks, and basics!"
    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 = """
<span style="color:#836953; font-size:24px; font-family:Roboto;">🌱Welcome to CompBot!</span>
""""""
## Your AI-driven assistant for all composting-related queries.
"""

topics = """
### Feel free to ask me anything from the topics below!
- Components of composting
- Green and brown materials
- The composting process 
- Common strategies
- Uses of compost
- Tips for successful composting
- Sustainability
"""

# Define the HTML iframe content
podcast_iframe = '''
    <div style="height:10px;"></div>
    <iframe style="border-radius:12px" 
    src="https://open.spotify.com/embed/episode/1Emjgqf8PfwD42kvyKvtfW?utm_source=generator&theme=0" 
    width="100%" height="152" frameBorder="0" 
    allow="autoplay; clipboard-write; encrypted-media; fullscreen; picture-in-picture" loading="lazy"></iframe>
    <div style="height:20px;"></div>
    <iframe style="border-radius:12px" 
    src="https://open.spotify.com/embed/episode/6m83iwiAwCOu5yaW8LOT1v?utm_source=generator&theme=0" 
    width="100%" height="152" frameBorder="0" allowfullscreen="" 
    allow="autoplay; clipboard-write; encrypted-media; fullscreen; picture-in-picture" loading="lazy"></iframe>
'''

youtube_iframe = '''
    <iframe width="560" height="315" src="https://www.youtube.com/embed/MryNKPPvFbk" frameborder="0" 
    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
'''

def display_image():
    return "https://huggingface.co/spaces/dogutcu/composting-how-tos/resolve/main/compbot.jpeg"


custom_css = """
<style>
    .textbox-question {
        background-color: #E8F0FE !important;  /* Light blue background */
    }
    .textbox-answer {
        background-color: #F1F8E9 !important;  /* Light green background */
    }
</style>
"""

theme = gr.themes.Base().set(
    background_fill_primary='#AFC9AD',  # Light cyan background
    background_fill_primary_dark='#AFC9AD',  # Dark teal background
    background_fill_secondary='#ffccbc',  # Light orange background
    background_fill_secondary_dark='#d84315',  # Dark orange background
    border_color_accent='#ffab40',  # Accent border color
    border_color_accent_dark='#ff6d00',  # Dark accent border color
    border_color_accent_subdued='#ff8a65',  # Subdued accent border color
    border_color_primary='#2a2a2a',  # Primary border color
    block_border_color='#2a2a2a',  # Block border color
    button_primary_background_fill='#2a2a2a',  # Primary button background color
    button_primary_background_fill_dark='#2a2a2a'  # Dark primary button background color
)
    
# Setup the Gradio Blocks interface with custom layout components
with gr.Blocks(theme=theme) as demo:
    gr.HTML(custom_css)
    gr.Image(display_image(), show_label = False, show_share_button = False, show_download_button = False, width=300, height=200)
    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
            gr.HTML(youtube_iframe)  # Embed the iframe on the left side
        with gr.Row():
            with gr.Column():
                question = gr.Textbox(label="Your question", placeholder="What would you like to know?")
                answer = gr.Textbox(label="CompBot Response", placeholder="CompBot will respond here...", interactive=False, lines=16)
                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)