Update app.py

app.py

@@ -1,6 +1,6 @@
 import gradio as gr
 from sentence_transformers import SentenceTransformer, util
-import openai  
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
 import os
 
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
@@ -9,17 +9,18 @@ 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/'
 
-#openai.api_key = os.environ["OPENAI_API_KEY"]
+# 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 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
+# Load the retrieval model
 try:
     retrieval_model = SentenceTransformer(retrieval_model_name)
     print("Models loaded successfully.")
@@ -27,9 +28,6 @@ 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()]
@@ -42,56 +40,29 @@ 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.
-    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 therapy, destressing activites, and student opportunities information.
-    """
     try:
         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()
-        
+
+        # Encode the input and generate a response
+        input_ids = tokenizer.encode(user_message, return_tensors='pt')
+        output = model.generate(input_ids, max_length=150, num_return_sequences=1)
+        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
         
     except Exception as e:
@@ -99,9 +70,6 @@ def generate_response(user_query, relevant_segment):
         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 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)
@@ -111,112 +79,33 @@ def query_model(question):
     return response
 
 # Define the HTML iframe content
-iframe = '''
-<iframe style="border-radius:12px" src="https://docs.google.com/spreadsheets/d/e/2PACX-1vRroWVBXq1Fa0x7SvRTzSBMHFIp59VtVEWCxeg8kWJU4ll1_o4yzBnt4ArT88s7g4TQrMKEXZUQAeHF/pubhtml?widget=true&amp;headers=false" width="100%" height="352" frameBorder="0" allowfullscreen="true" allow="autoplay; clipboard-write; encrypted-media; fullscreen; picture-in-picture" loading="lazy"></iframe>
-'''
-
-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>
-'''
-
+# (Your iframe content goes here)
 
 # 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
-- Technology / Computer Science
-- Research : STEM
-- Finance
-- Law / Political Science / Debate
-- The Arts
-- Business / Leadership
-- Pyschology
-- Medicine / Biology
-- Literature / Writing
-- College Prep
-- Advocacy: Non-Profit, Environment or Identity 
-- Volunteering 
-- Study Abroad
-"""
-
-topics2= """
-### Feel Free to ask CalmBot (Our Therapist Bot) anything from the topics below!
-- Arts and Crafts (When asking for arts and crafts ideas, state whether you have 15 min, 30 min, 45 min, 1 hour, 1 hour and a half, 2 hours, 2 hours and a half, 3 hours or greater)
-- Destressing strategies (Breathing Exercises, stretches, etc.)
-- Mental Health
-- Identity (Sexual, Gender, etc.)
-- Bullying
-- Racism
-- Relationships (Family, Friends, etc.)
-- Abuse (Emotional, Physical, Sexual, Mental, etc.)
-- Support Resources
-"""
-
-# Create a Gradio HTML component
-def display_iframe():
-    return iframe
-
-def display_iframe2():
-    return iframe2
-
-
-theme = gr.themes.Default(
-    primary_hue="neutral",
-    secondary_hue="neutral",
-).set(
-    background_fill_primary='#e3e9da',
-    background_fill_primary_dark='#e3e9da',
-    background_fill_secondary="#f8f1ea",
-    background_fill_secondary_dark="#f8f1ea",
-    border_color_accent="#f8f1ea",
-    border_color_accent_dark="#e3e9da",
-    border_color_accent_subdued="#f8f1ea",
-    border_color_primary="#f8f1ea",
-    block_border_color="#f8f1ea",
-    button_primary_background_fill="#f8f1ea",
-    button_primary_background_fill_dark="#f8f1ea"
-)
-    
+# (Your welcome message and topics go here)
+
 # Setup the Gradio Blocks interface with custom layout components
-with gr.Blocks(theme=theme) as demo:
+with gr.Blocks() as demo:
     gr.Image("CalmConnect.jpg", show_label=False, show_share_button=False, show_download_button=False)
-    gr.Markdown(welcome_message)  # Display the formatted welcome message
-    
+    gr.Markdown(welcome_message)
+
     with gr.Row():
         with gr.Column():
-            gr.Markdown(topics)  # Show the topics on the left side
-            gr.HTML(iframe)  # Embed the iframe on the left side
-            gr.HTML(iframe2)  # Embed the iframe on the right side
+            gr.Markdown(topics)
+            gr.HTML(iframe)
+            gr.HTML(iframe2)
         
         with gr.Column():
-            gr.Markdown(topics2)  # Show the topics on the left side
+            gr.Markdown(topics2)
             with gr.Row():
                 with gr.Column():
                     question = gr.Textbox(label="You", placeholder="What do you want to talk to CalmBot about?")
                     answer = gr.Textbox(label="CalmBot's Response :D", placeholder="CalmBot will respond here..", interactive=False, lines=20)
                     submit_button = gr.Button("Submit")
                     submit_button.click(fn=query_model, inputs=question, outputs=answer)
-    
-    with gr.Row():
-        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>")
-
-
-demo.launch()
-
-
-
 
+    with gr.Row():
+        # (Your buttons go here)
 
 # Launch the Gradio app to allow user interaction
-demo.launch(share=True)
+demo.launch(share=True)