Update app.py

app.py

@@ -2,6 +2,7 @@ import gradio as gr
 from sentence_transformers import SentenceTransformer, util
 import openai
 import os
+import random  # Import the random library
 
 os.environ["TOKENIZERS_PARALLELISM"] = "false"
 
@@ -11,7 +12,7 @@ retrieval_model_name = 'output/sentence-transformer-finetuned/'
 
 openai.api_key = os.environ["OPENAI_API_KEY"]
 
-system_message = "You use keywords such as modest or not modest,comfort level (1=comfortable, 2=everyday wear, 3=formal), color, and occasion inputted by users and outputting a list of simple clothing pieces (consisting of a top, bottom, and possibly accessories and outerwear) as well as the pinterest link to the outfit created, resulting in a cohesive outfit. Consider season first then consider the other keywords the user inputs."
+system_message = "You put together outfits by taking keywords such as modest or not modest,comfort level (1=comfortable, 2=everyday wear, 3=formal), color, and occasion inputted by users and outputting a list of simple clothing pieces (consisting of a top, bottom, and possibly accessories and outerwear) and a Pinterest link to the outfit created, resulting in a cohesive outfit."
 # Initial system message to set the behavior of the assistant
 messages = [{"role": "system", "content": system_message}]
 
@@ -37,10 +38,9 @@ def load_and_preprocess_text(filename):
 
 segments = load_and_preprocess_text(filename)
 
-def find_relevant_segment(user_query, segments):
+def find_relevant_segments(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.
+    Find the most relevant text segments for a user's query using cosine similarity among sentence embeddings.
     """
     try:
         # Lowercase the query for better matching
@@ -53,21 +53,24 @@ def find_relevant_segment(user_query, 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()
+        # Get indices of the most similar segments
+        best_indices = similarities.topk(5).indices.tolist()
         
-        # Return the most relevant segment
-        return segments[best_idx]
+        # Return the most relevant segments
+        return [segments[idx] for idx in best_indices]
     except Exception as e:
-        print(f"Error in finding relevant segment: {e}")
-        return ""
+        print(f"Error in finding relevant segments: {e}")
+        return []
 
-def generate_response(user_query, relevant_segment):
+def generate_response(user_query, relevant_segments):
     """
     Generate a response emphasizing the bot's capability in providing fashion information.
     """
     try:
-        user_message = f"Of course! Here are your outfit suggestions and some sustainable brands you can buy from: {relevant_segment}"
+        # Randomly select an outfit from the relevant segments
+        random_segment = random.choice(relevant_segments)
+        
+        user_message = f"Of course! Here are your outfit suggestions and some sustainable brands you can buy from: {random_segment}"
 
         # Append user's message to messages list
         messages.append({"role": "user", "content": user_message})
@@ -76,7 +79,7 @@ def generate_response(user_query, relevant_segment):
             model="gpt-3.5-turbo",
             messages=messages,
             max_tokens=150,
-            temperature=0.3,
+            temperature=0.4,
             top_p=1,
             frequency_penalty=0,
             presence_penalty=0
@@ -100,17 +103,16 @@ def query_model(question):
     """
     if question == "":
         return "Welcome to Savvy! Use the word bank to describe the outfit you would like generated."
-    relevant_segment = find_relevant_segment(question, segments)
-    if not relevant_segment:
+    relevant_segments = find_relevant_segments(question, segments)
+    if not relevant_segments:
         return "I'm sorry. Could you be more specific? Check your spelling and make sure to use words from the bank."
-    response = generate_response(question, relevant_segment)
+    response = generate_response(question, relevant_segments)
     return response
 
 # Define the welcome message and specific topics the chatbot can provide information about
 welcome_message = """
 # 🌷 Welcome to Savvy!
-
-## You can ask our SustainaBot to find eco-friendly brands, make outfits based on season and aesthetic, and to learn more about the detriments of fast fashion. You can also learn how to contribute to circular fashion by scrolling down. Created by Sarah, Medha, Nicole, and Tegen of the 2024 Kode With Klossy SEATTLE Camp. 
+## You can ask our SustainaBot to find eco-friendly brands, make outfits based on season and aesthetic, and to learn more about the detriments of fast fashion. You can also learn how to contribute to circular fashion by scrolling down. Created by Sarah, Medha, Nicole, and Tegen of the 2024 Kode With Klossy CITY Camp. 
 """
 
 topics = """
@@ -132,7 +134,6 @@ with gr.Blocks(theme='JohnSmith9982/small_and_pretty') as demo:
             answer = gr.Textbox(label="Sustainabot Response", placeholder="Sustainabot will respond here...", interactive=False, lines=10)
             submit_button = gr.Button("Submit")
             submit_button.click(fn=query_model, inputs=question, outputs=answer)
-    
 
 # Launch the Gradio app to allow user interaction
-demo.launch(share=True)
+demo.launch(share=True)