Update app.py

app.py

@@ -13,80 +13,63 @@ def find_closest_neighbors(vector1, dictionary_of_vectors):
     """
     Takes a vector and a dictionary of vectors and returns the three closest neighbors
     """
-
-    # Convert the input string to a vector
     vector = openai.Embedding.create(
         input=vector1,
         engine="text-embedding-ada-002"
     )['data'][0]['embedding']
-    
+
     vector = np.array(vector)
 
-    # Finds cosine similarities between the vector and values in the dictionary and Creates a dictionary of cosine similarities with its text key
     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]
 
-    # Sorts the dictionary by value and returns the three highest values
     sorted_cosine_similarities = sorted(cosine_similarities.items(), key=lambda x: x[1], reverse=True)
     match_list = sorted_cosine_similarities[0:4]
-    web = str(sorted_cosine_similarities[0][0])
-    return match_list
-
-# Connect to the database
-conn = sqlite3.connect('QRIdatabase7.db')
-
-# Create a cursor
-cursor = conn.cursor()
-
-# Select the text and embedding from the chunks table
-cursor.execute('''SELECT text, embedding FROM chunks''')
-
-# Fetch the rows
-rows = cursor.fetchall()
-
-# Create a dictionary to store the text and embedding for each row
-dictionary_of_vectors = {}
 
-# Iterate through the rows and add them to the dictionary
-for row in rows:
-    text = row[0]
-    embedding_str = row[1]
-    # Convert the embedding string to a NumPy array
-    embedding = np.fromstring(embedding_str, sep=' ')
-    dictionary_of_vectors[text] = embedding
+    return match_list
 
-# Close the connection
-conn.close()
-
-def context_gpt_response(question):
-    """
-    Takes a question and returns an answer
-    """
+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(question, dictionary_of_vectors)
-
-    # Create a string of the text from the closest neighbors
+    match_list = find_closest_neighbors(message, dictionary_of_vectors)
     context = ''
     for match in match_list:
-        context += str(match[0])  
+        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:  {question}  A: "
-    # Generate an answer
-    response = openai.Completion.create(
-        engine="gpt-4",
-        prompt=prep,
-        temperature=0.7,
-        max_tokens=220,
-    )
-
+    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: "
 
-    # Return the answer
-    return response['choices'][0]['text']
+    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})
 
-import gradio as gr
+    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 
 
-iface = gr.Interface(fn=context_gpt_response, inputs="text", outputs="text",title="Qualia Research Institute GPTbot", description="Ask any question and get QRI specific answers!", examples=[["What is QRI?"], ["What is the Symmetry Theory of Valence?"], ["Explain Logarithmic scales of pain and pleasure"]])
-iface.launch()
+gr.ChatInterface(predict).queue().launch()