app.py

import gradio as gr
from sentence_transformers import SentenceTransformer, util
import openai
import os
import random  # Import the random library

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 put together outfits by taking keywords such as modest or not modest,comfort level (1=comfortable, 2=everyday wear, 3=formal), color, and occasion inputted by users and outputting a list of simple clothing pieces (consisting of a top, bottom, and possibly accessories and outerwear) and a Pinterest link to the outfit created, resulting in a cohesive outfit."
# 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_segments(user_query, segments):
    """
    Find the most relevant text segments for a user's query using cosine similarity among sentence embeddings.
    """
    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]
        
        # Get indices of the most similar segments
        best_indices = similarities.topk(5).indices.tolist()
        
        # Return the most relevant segments
        return [segments[idx] for idx in best_indices]
    except Exception as e:
        print(f"Error in finding relevant segments: {e}")
        return []

def generate_response(user_query, relevant_segments):
    """
    Generate a response emphasizing the bot's capability in providing fashion information.
    """
    try:
        # Randomly select an outfit from the relevant segments
        random_segment = random.choice(relevant_segments)
        
        user_message = f"Of course! Here are your outfit suggestions and some sustainable brands you can buy from: {random_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=0.4,
            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 Savvy! Use the word bank to describe the outfit you would like generated."
    relevant_segments = find_relevant_segments(question, segments)
    if not relevant_segments:
        return "I'm sorry. Could you be more specific? Check your spelling and make sure to use words from the bank."
    response = generate_response(question, relevant_segments)
    return response

# Define the welcome message and specific topics the chatbot can provide information about
welcome_message = """
"""

topics = """
"""

pinterest = """
<a data-pin-do="embedPin" href="https://www.pinterest.com/pin/219620919322613000/"></a>
    <script async type="text/javascript" src="https://assets.pinterest.com/js/pinit.js">
"""

# 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
            question = gr.Textbox(label="Your question", placeholder="What do you want to ask about?")
            answer = gr.Textbox(label="Sustainabot Response", placeholder="Sustainabot will respond here...", interactive=False, lines=10)
            submit_button = gr.Button("Submit")
            submit_button.click(fn=query_model, inputs=question, outputs=answer)
    # with gr.Row():
    #     with gr.Column():
            

# Launch the Gradio app to allow user interaction
demo.launch(share=True)