app.py

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 restaurant recommending chatbot that suggests one restaurant based on the criteria the user provides."
# 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 suggesting a restaurant.
    """
    try:
        user_message = f"Here is a local restaurant based on your information: {relevant_segment}"
        # Append user's message to messages list
        messages.append({"role": "user", "content": user_message})
        response = openai.ChatCompletion.create(
            model="gpt-4o",
            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 "Give me your preferences..."
    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 Ethical Eats Explorer!
## Your AI-driven assistant for restaurant recs in Seattle. Created by Saranya, Cindy, and Liana of the 2024 Kode With Klossy Seattle Camp.
"""
topics = """
### Please give me your restaurant preferences:
- Dietary Restrictions
- Cuisine Preferences (optional)
    - Cuisines: American, Indian, Middle Eastern, Chinese, Italian, Thai, Hawaiian-Korean, Japanese, Ethiopian, Pakistani, Mexican, Ghanaian, Vietnamese, Filipino, Spanish, Turkish
- Budget Preferences (Low: $0 - $20, Moderate: $20 - $30, High: $30+ - per person)
Please send your message in the format: "Could you give me a (cuisine) restaurant with (dietary restriction) options that is (budget) budget?"
"""
# 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="Give me your information...")
            answer = gr.Textbox(label="Explorer's Response", placeholder="Explorer 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)