test / app.py
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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_chess_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 in providing chess information.
"""
try:
system_message = "You are a chess chatbot specialized in providing information on chess rules, strategies, and terminology."
user_message = f"Here's the information on chess: {relevant_segment}"
messages = [
{"role": "system", "content": system_message},
{"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
)
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 ChessBot! Ask me anything about chess rules, strategies, and terminology."
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 ChessBot!
## Your AI-driven assistant for all chess-related queries.
"""
topics = """
### Feel Free to ask me anything from the topics below!
- Chess piece movements
- Special moves
- Game phases
- Common strategies
- Chess terminology
- Famous games
- Chess tactics
"""
# 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="ChessBot Response", placeholder="ChessBot 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)