Update app.py

app.py

@@ -1,89 +1,92 @@
-import openai
-import gradio as gr
+
+import sklearn
 import sqlite3
 import numpy as np
 from sklearn.metrics.pairwise import cosine_similarity
+import openai
+import os
+
+openai.api_key = os.environ["Secret"] 
 
-openai.api_key = "sk-..."  # Replace with your key
 
-def find_closest_neighbors(vector, dictionary_of_vectors):
+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
 
-def handle_input(user_input):
+# 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
+
+# Close the connection
+conn.close()
+
+def context_gpt_response(question):
     """
-    Checks if the user input is a text file or a string.
-    If it's a text file, it reads the file, splits it into 250-character chunks, and returns the chunks.
-    If it's a string, it just returns the string.
+    Takes a question and returns an answer
     """
-    if isinstance(user_input, gr.inputs.File):
-        with open(user_input.name, 'r') as file:
-            text = file.read()
-        chunks = [text[i:i+250] for i in range(0, len(text), 250)]
-        return chunks
-    else:
-        return [user_input]
-
-def predict(user_input, history):
-    # Connect to the database
-    conn = sqlite3.connect('QRIdatabase7 (1).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()
-
-    input_chunks = handle_input(user_input)
-
-    for message in input_chunks:
-        # Create embedding for the message
-        message_vector = openai.Embedding.create(
-            input=message,
-            engine="text-embedding-ada-002"
-        )['data'][0]['embedding']
-        message_vector = np.array(message_vector)
-        
-        # Find the closest neighbors
-        match_list = find_closest_neighbors(message_vector, 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-4',
-            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 
-
-gr.ChatInterface(predict, inputs=gr.inputs.Mixed([gr.inputs.Textbox(lines=3), gr.inputs.File()]), allow_flagging=False).queue().launch()
+
+    # Find the closest neighbors
+    match_list = find_closest_neighbors(question, dictionary_of_vectors)
+
+    # Create a string of the text from the closest neighbors
+    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:  {question}  A: "
+    # Generate an answer
+    response = openai.Completion.create(
+        engine="gpt-4",
+        prompt=prep,
+        temperature=0.7,
+        max_tokens=220,
+    )
+
+
+    # Return the answer
+    return response['choices'][0]['text']
+
+import gradio as gr
+
+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()