Update app.py

app.py

@@ -2,31 +2,25 @@ import sklearn
 import sqlite3
 import numpy as np
 from sklearn.metrics.pairwise import cosine_similarity
-import openai
+from openai import OpenAI
 import os
 import gradio as gr
 
-
-openai.api_key = os.environ["Secret"] 
+client = OpenAI(api_key=os.environ["Secret"])
 
 def find_closest_neighbors(vector1, dictionary_of_vectors):
-    """
-    Takes a vector and a dictionary of vectors and returns the three closest neighbors
-    """
-    vector = openai.Embedding.create(
+    """Takes a vector and a dictionary of vectors and returns the three closest neighbors"""
+    vector = client.embeddings.create(
         input=vector1,
-        engine="text-embedding-ada-002"
-    )['data'][0]['embedding']
-
+        model="text-embedding-ada-002"
+    ).data[0].embedding
+    
     vector = np.array(vector)
-
     cosine_similarities = {}
     for key, value in dictionary_of_vectors.items():
         cosine_similarities[key] = cosine_similarity(vector.reshape(1, -1), value.reshape(1, -1))[0][0]
-
     sorted_cosine_similarities = sorted(cosine_similarities.items(), key=lambda x: x[1], reverse=True)
     match_list = sorted_cosine_similarities[0:4]
-
     return match_list
 
 def predict(message, history):
@@ -35,7 +29,6 @@ def predict(message, history):
     cursor = conn.cursor()
     cursor.execute('''SELECT text, embedding FROM chunks''')
     rows = cursor.fetchall()
-
     dictionary_of_vectors = {}
     for row in rows:
         text = row[0]
@@ -43,44 +36,47 @@ def predict(message, history):
         embedding = np.fromstring(embedding_str, sep=' ')
         dictionary_of_vectors[text] = embedding
     conn.close()
-
+    
     # Find the closest neighbors
     match_list = find_closest_neighbors(message, dictionary_of_vectors)
     context = ''
     for match in match_list:
         context += str(match[0])
-    context = context[:-1500]
-
-    prep = f"This is an OpenAI model tuned to answer questions specific to the Qualia Research institute, a research institute that focuses on consciousness. Here is some question-specific context, and then the Question to answer, related to consciousness, the human experience, and phenomenology: {context}. Here is a question specific to QRI and consciousness in general Q:  {message}  A: "
-
-    history_openai_format = []
+    context = context[:1500]  # Limit context length
+    
+    prep = f"This is an OpenAI model tuned to answer questions specific to the Qualia Research institute, a research institute that focuses on consciousness. Here is some question-specific context, and then the Question to answer, related to consciousness, the human experience, and phenomenology: {context}. Here is a question specific to QRI and consciousness in general Q: {message} A: "
+    
+    messages = []
+    # Convert history to the expected format
     for human, assistant in history:
-        history_openai_format.append({"role": "user", "content": human })
-        history_openai_format.append({"role": "assistant", "content":assistant})
-    history_openai_format.append({"role": "user", "content": prep})
-
-    response = openai.ChatCompletion.create(
-        model='gpt-3.5-turbo',
-        messages= history_openai_format,         
+        messages.append({"role": "user", "content": human})
+        messages.append({"role": "assistant", "content": assistant})
+    messages.append({"role": "user", "content": prep})
+    
+    stream = client.chat.completions.create(
+        model="gpt-3.5-turbo",
+        messages=messages,
         temperature=1.0,
         stream=True
     )
     
     partial_message = ""
-    for chunk in response:
-        if len(chunk['choices'][0]['delta']) != 0:
-            partial_message = partial_message + chunk['choices'][0]['delta']['content']
-            yield partial_message 
+    for chunk in stream:
+        if chunk.choices[0].delta.content is not None:
+            partial_message += chunk.choices[0].delta.content
+            yield partial_message
 
-# Adjust the Gradio interface for a chatbot
-demo = gr.Interface(
-fn=predict,
-    inputs=[
-        gr.Textbox(label="Message", placeholder="Enter your message"),
-      gr.State(label="Conversation History")  # State is used to manage history
-   ],
-    outputs=gr.Textbox(label="Response"),
-    live=True).queue()
+with gr.Blocks(title="QRI Research Assistant") as demo:
+    chatbot = gr.ChatInterface(
+        predict,
+        title="QRI Research Assistant",
+        description="Ask questions about consciousness, human experience, and phenomenology based on QRI research.",
+        examples=[
+            "What is consciousness?",
+            "How does QRI approach the study of phenomenology?",
+            "What are the key theories about qualia?"
+        ]
+    )
 
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()