Update app.py

app.py

@@ -2,9 +2,8 @@ import gradio as gr
 from sentence_transformers import SentenceTransformer, util
 import openai
 import os
-import os
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
+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
@@ -12,7 +11,6 @@ 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)
@@ -38,41 +36,41 @@ 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 tries to match country names in the query with those in the segments.
+    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()
-        # Filter segments to include only those containing country names mentioned in the query
-        country_segments = [seg for seg in segments if any(country.lower() in seg.lower() for country in ['Guatemala', 'Mexico', 'U.S.', 'United States'])]
-        
-        # If no specific country segments found, default to general matching
-        if not country_segments:
-            country_segments = segments
         
+        # Encode the query and the segments
         query_embedding = retrieval_model.encode(lower_query)
-        segment_embeddings = retrieval_model.encode(country_segments)
+        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 country_segments[best_idx]
+        
+        # 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 country-specific visa information.
+    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 visa requirements for your query: {relevant_segment}"
+        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",  # Verify model name
+            model="gpt-3.5-turbo",
             messages=messages,
             max_tokens=150,
             temperature=0.2,
@@ -85,14 +83,9 @@ def generate_response(user_query, relevant_segment):
         print(f"Error in generating response: {e}")
         return f"Error in generating response: {e}"
 
-
-
-
-# Define and configure the Gradio application interface to interact with users.
-# Define and configure the Gradio application interface to interact with users.
 def query_model(question):
     """
-     Process a question, find relevant information, and generate a response.
+    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."
@@ -102,10 +95,7 @@ def query_model(question):
     response = generate_response(question, relevant_segment)
     return response
 
-
-
-
-# Define the welcome message and specific topics and countries the chatbot can provide information about.
+# Define the welcome message and specific topics the chatbot can provide information about
 welcome_message = """
 # Welcome to ChessBot!
 
@@ -123,27 +113,6 @@ topics = """
 - Chess tactics
 """
 
-
-# Define and configure the Gradio application interface to interact with users.
-def query_model(question):
-    """
-    Process a question, find relevant information, and generate a response.
-    
-    Args:
-        question (str): User input question.
-
-    Returns:
-        str: Generated response or a default welcome message if no question is provided.
-    """
-    if question == "":
-        return welcome_message
-    relevant_segment = find_relevant_segment(question, segments)
-    response = generate_response(question, relevant_segment)
-    return response
-
-
-
-
 # Setup the Gradio Blocks interface with custom layout components
 with gr.Blocks() as demo:
     gr.Markdown(welcome_message)  # Display the formatted welcome message
@@ -161,4 +130,3 @@ with gr.Blocks() as demo:
 
 # Launch the Gradio app to allow user interaction
 demo.launch(share=True)
-