Update app.py

app.py

@@ -1,12 +1,79 @@
-import time
+import sklearn
+import sqlite3
+import numpy as np
+from sklearn.metrics.pairwise import cosine_similarity
+import openai
+import os
 import gradio as gr
 
-def slow_echo(message, history):
-    for i in range(len(message)):
-        time.sleep(0.05)
-        yield "You typed: " + message[: i+1]
 
-demo = gr.ChatInterface(slow_echo).queue()
+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(
+        input=vector1,
+        engine="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):
+    # Connect to the database
+    conn = sqlite3.connect('QRIdatabase7.db')
+    cursor = conn.cursor()
+    cursor.execute('''SELECT text, embedding FROM chunks''')
+    rows = cursor.fetchall()
+
+    dictionary_of_vectors = {}
+    for row in rows:
+        text = row[0]
+        embedding_str = row[1]
+        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 = []
+    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,         
+        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 
+
+demo = gr.ChatInterface(predict).queue()
 
 if __name__ == "__main__":
     demo.launch()
+