Update app.py

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 from sentence_transformers import SentenceTransformer, util
-from transformers import GPT2LMHeadModel, GPT2Tokenizer
+import openai  
 import os
 
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
@@ -9,17 +9,14 @@ os.environ["TOKENIZERS_PARALLELISM"] = "false"
 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
-gpt2_model_name = "gpt2"
-tokenizer = GPT2Tokenizer.from_pretrained(gpt2_model_name)
-gpt2_model = GPT2LMHeadModel.from_pretrained(gpt2_model_name)
+#openai.api_key = os.environ["OPENAI_API_KEY"]
 
+system_message = "You are a comfort chatbot specialized in providing information on therapy, destressing activites, and student opportunities."
 # Initial system message to set the behavior of the assistant
-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"
+	"role": "system",
+	"content": "Do not use Markdown Format. Do not include hashtags or asterisks"
 })
 
 # Attempt to load the necessary models and provide feedback on success or failure
@@ -30,7 +27,9 @@ 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."""
+    """
+    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()]
@@ -43,7 +42,10 @@ def load_and_preprocess_text(filename):
 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."""
+    """
+    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()
@@ -65,31 +67,41 @@ def find_relevant_segment(user_query, segments):
         return ""
 
 def generate_response(user_query, relevant_segment):
-    """Generate a response using GPT-2 emphasizing the bot's capability in providing therapy, destressing activities, and student opportunities information."""
+    """
+    Generate a response emphasizing the bot's capability in providing therapy, destressing activites, and student opportunities information.
+    """
     try:
-        user_message = f"Here's the information on your request: {relevant_segment}. Now I will elaborate:"
-
-        # Encode the input text and generate a response
-        input_ids = tokenizer.encode(user_message, return_tensors='pt')
-        output = gpt2_model.generate(
-            input_ids,
-            max_length=500,
-            num_return_sequences=1,
-            no_repeat_ngram_size=2,
-            early_stopping=True
+        user_message = f"Here's the information on your request: {relevant_segment}"
+
+        # Append user's message to messages list
+        messages.append({"role": "user", "content": user_message})
+        
+        response = openai.ChatCompletion.create(
+            model="gpt-3.5-turbo",  # or "gpt-4" if you have access
+            messages=messages,
+            max_tokens=500,
+            temperature=0.5,
+            top_p=1,
+            frequency_penalty=0.5,
+            presence_penalty=0.5,
         )
+                
+        # Extract the response text
+        output_text = response['choices'][0]['message']['content'].strip()
         
-        # Decode the generated response
-        output_text = tokenizer.decode(output[0], skip_special_tokens=True)
+        # Append assistant's message to messages list for context
+        messages.append({"role": "assistant", "content": output_text})
         
-        return output_text.strip()
+        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."""
+    """
+    Process a question, find relevant information, and generate a response.
+    """
     if question == "":
         return "Welcome to CalmConnect! Ask me anything about destressing strategies or student opportunities. Feel free to talk to our online therapist!"
     relevant_segment = find_relevant_segment(question, segments)
@@ -107,11 +119,16 @@ iframe2 = '''
 <iframe style="border-radius:12px" src="https://open.spotify.com/embed/playlist/6wwxTePuIKYMqt6RCytB7X?utm_source=generator" width="100%" height="300" frameBorder="0" allowfullscreen="" allow="autoplay; clipboard-write; encrypted-media; fullscreen; picture-in-picture" loading="lazy"></iframe>
 '''
 
+
 # Define the welcome message and specific topics the chatbot can provide information about
 welcome_message = """
 <span style="color:#718355; font-size:24px; font-weight:bold;"> 🪷 Welcome to CalmConnect! 🪷</span>
 """
 
+"""
+## Your AI-driven assistant for destressing and extracurricular opportunity queries. Created by Olivia W, Alice T, and Cindy W of the 2024 Kode With Klossy CITY Camp. 
+"""
+
 topics = """
 ### If you are interested in the following below, click on our Student Opportunities Database!
 - Engineering
@@ -121,7 +138,7 @@ topics = """
 - Law / Political Science / Debate
 - The Arts
 - Business / Leadership
-- Psychology
+- Pyschology
 - Medicine / Biology
 - Literature / Writing
 - College Prep
@@ -192,10 +209,7 @@ with gr.Blocks(theme=theme) as demo:
         big_block = gr.HTML("### <button><a href='https://www.headspace.com/teens'>FREE: HEADSPACE FOR TEENS </a></button>")
         big_block2 = gr.HTML("<button><a href='https://calmconnect-flower.replit.app/'>PLAY FLOWER GAME</a></button>")
         big_block3 = gr.HTML("<button><a href='https://www.nyc.gov/site/doh/health/health-topics/teenspace.page'>NYC: TEENSPACE (free services)</a></button>")
-        big_block4 = gr.HTML("<button><a href='https://www.teenlife.com/blog/mental-health-resources-for-teens/'>TEEN MENTAL HEALTH RESOURCES (free services)</a></button>")
-
-# Launch the Gradio app to
-
+        big_block4 =gr.HTML("<button><a href='https://www.teenlife.com/blog/mental-health-resources-for-teens/'>TEEN MENTAL HEALTH RESOURCES (free services)</a></button>")
 
 
 demo.launch()