Update app.py

app.py

@@ -4,31 +4,31 @@ from transformers import GPT2LMHeadModel, GPT2Tokenizer
 import os
 import torch
 
-import os
-import openai
-
-# Ensure the OpenAI API key is set
-openai.api_key = os.getenv("OPENAI_API_KEY")
+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 destress-specific details
+retrieval_model_name = 'output/sentence-transformer-finetuned/'
+
+# Load GPT-2 model and tokenizer
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+model = GPT2LMHeadModel.from_pretrained("gpt2")
 
 system_message = "You are a comfort chatbot specialized in providing information on therapy, destressing activities, and student opportunities."
 messages = [{"role": "system", "content": system_message}]
 messages.append({
     "role": "system",
-    "content": "Do not use Markdown Format. Do not include hashtags or asterisks."
+    "content": "Do not use Markdown Format. Do not include hashtags or asterisks"
 })
 
+# Load the retrieval model
+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 data from a file.
-    
-    Args:
-    - filename (str): Path to the text file to be loaded.
-    
-    Returns:
-    - list[str]: List of preprocessed text segments.
-    """
     try:
         with open(filename, 'r', encoding='utf-8') as file:
             segments = [line.strip() for line in file if line.strip()]
@@ -40,31 +40,6 @@ def load_and_preprocess_text(filename):
 
 segments = load_and_preprocess_text(filename)
 
-def generate_response_gpt4(user_query, relevant_segment):
-    try:
-        # Construct the messages for the chat history
-        messages.append({"role": "user", "content": user_query})
-        messages.append({"role": "assistant", "content": relevant_segment})
-
-        # Use OpenAI's API to generate a response
-        response = openai.ChatCompletion.create(
-            model="gpt-4",
-            messages=messages,
-            max_tokens=150,  # Control the length of the response
-            temperature=0.7  # Adjust creativity as needed
-        )
-
-        # Get the assistant's response from the generated content
-        assistant_reply = response['choices'][0]['message']['content']
-        
-        # Append the assistant's response to the conversation history
-        messages.append({"role": "assistant", "content": assistant_reply})
-        return assistant_reply
-
-    except Exception as e:
-        print(f"Error in generating response: {e}")
-        return f"Error in generating response: {e}"
-
 def find_relevant_segment(user_query, segments):
     try:
         lower_query = user_query.lower()
@@ -107,6 +82,7 @@ def generate_response(user_query, relevant_segment):
         print(f"Error in generating response: {e}")
         return f"Error in generating response: {e}"
 
+
 def query_model(question):
     if question == "":
         return "Welcome to CalmConnect! Ask me anything about destressing strategies or student opportunities. Feel free to talk to our online therapist!"