Update app.py

app.py

@@ -4,31 +4,31 @@ from transformers import GPT2LMHeadModel, GPT2Tokenizer
 import os
 import torch
 
-os.environ["TOKENIZERS_PARALLELISM"] = "false"
+import os
+import openai
 
-# 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/'
+# Ensure the OpenAI API key is set
+openai.api_key = os.getenv("OPENAI_API_KEY")
 
-# Load GPT-2 model and tokenizer
-tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
-model = GPT2LMHeadModel.from_pretrained("gpt2")
+filename = "output_topic_details.txt"  # Path to the file storing destress-specific details
 
 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()]
@@ -40,6 +40,31 @@ 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()