Update app.py

app.py

@@ -1,7 +1,6 @@
 import gradio as gr
 from sentence_transformers import SentenceTransformer, util
 import openai
-from openai import OpenAI
 import os
 
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
@@ -9,62 +8,12 @@ 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 chess-specific details
 retrieval_model_name = 'output/sentence-transformer-finetuned/'
-
-client = OpenAI(api_key="DEEPSEEK_API", base_url="https://api.deepseek.com")
+openai.api_key = os.environ["OPENAI_API_KEY"]
 
 system_message = "You are a chatbot specialized in providing information the Young Yale Global Scholars program. You will be giving information to be applicants and scholars."
 # Initial system message to set the behavior of the assistant
 messages = [{"role": "system", "content": system_message}]
 
-# Attempt to load the necessary models and provide feedback on success or failure
-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 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()]
-        print("Text loaded and preprocessed successfully.")
-        return segments
-    except Exception as e:
-        print(f"Failed to load or preprocess text: {e}")
-        return []
-
-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 exercise information.
     """
     try:
@@ -72,33 +21,6 @@ def generate_response(user_query, relevant_segment):
 
         # Append user's message to messages list
         messages.append({"role": "user", "content": user_message})
-        
-        response = client.chat.completions.create(
-    model="deepseek-chat",
-    messages=[
-        {"role": "system", "content": "You are a helpful assistant"},
-        {"role": "user", "content": "Hello"},
-    ],
-    max_tokens=1024,
-    temperature=0.7,
-    stream=False
-)
-
-        
-        # Extract the response text
-        output_text = response['choices'][0]['message']['content'].strip()
-        
-        # Append assistant's message to messages list for context
-        messages.append({"role": "assistant", "content": output_text})
-        
-        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.
     """
     if question == "":
@@ -106,11 +28,6 @@ def query_model(question):
     relevant_segment = find_relevant_segment(question, segments)
     if not relevant_segment:
         return "Could not find specific information. Please refine your question."
-    response = generate_response(question, relevant_segment)
-    return response
-
-
-# Define the welcome message and specific topics the chatbot can provide information about
 welcome_message = """
 #
 
@@ -131,7 +48,6 @@ topics = """
 """
 
 
-
 # Setup the Gradio Blocks interface with custom layout components
 theme = gr.themes.Monochrome(
     primary_hue="blue",
@@ -139,17 +55,6 @@ theme = gr.themes.Monochrome(
 ).set(
     background_fill_primary='*primary_200',
     background_fill_primary_dark='*primary_200',
-    background_fill_secondary='*secondary_300',
-    background_fill_secondary_dark='*secondary_300',
-    border_color_accent='*secondary_200',
-    border_color_accent_dark='*secondary_600',
-    border_color_accent_subdued='*secondary_200',
-    border_color_primary='*secondary_300',
-    block_border_color='*secondary_200',
-    button_primary_background_fill='*secondary_300',
-    button_primary_background_fill_dark='*secondary_300',
-    body_text_color='black')
-    
 # Setup the Gradio Blocks interface with custom layout components
 with gr.Blocks(theme=theme) as demo:
     theme='gstaff/xkcd' 
@@ -158,8 +63,16 @@ with gr.Blocks(theme=theme) as demo:
     with gr.Row():
         with gr.Column():
             gr.Markdown(topics)  
+
+
     
             
+    with gr.Row():
+        with gr.Column():
+            question = gr.Textbox(label="Your question", placeholder="What do you want to ask about?")
+            answer = gr.Textbox(label="Ask YYGS's Response:", placeholder="askYYGS will respond here...", interactive=False, lines=10)
+            submit_button = gr.Button("Submit")
+            submit_button.click(fn=query_model, inputs=question, outputs=answer)
     with gr.Row():
         with gr.Column():
             question = gr.Textbox(label="Your question", placeholder="What do you want to ask about?")