Update app.py

app.py

@@ -1,35 +1,21 @@
-import openai
+import sklearn
 import sqlite3
 import numpy as np
 from sklearn.metrics.pairwise import cosine_similarity
-import gradio as gr
+import openai
 import os
+import gradio as gr
 
+# Set OpenAI API key from environment variable
+openai.api_key = os.environ["Secret"]
 
-# Your OpenAI API Key
-openai.api_key = os.environ["Secret"] 
-
-# Connect to the SQLite database
-db_path = "text_chunks_with_embeddings.db"  # Update with the path to your database
-conn = sqlite3.connect(db_path)
-cursor = conn.cursor()
-
-# Fetch the rows from the database
-cursor.execute("SELECT text, embedding FROM chunks")
-rows = cursor.fetchall()
-
-# Create a dictionary to store the text and embedding for each row
-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
-
-# Close the connection
-conn.close()
+def find_closest_neighbors(vector1, dictionary_of_vectors):
+    vector = openai.Embedding.create(
+        input=vector1,
+        engine="text-embedding-ada-002"
+    )['data'][0]['embedding']
+    vector = np.array(vector)
 
-def find_closest_neighbors(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]
@@ -37,33 +23,47 @@ def find_closest_neighbors(vector):
     sorted_cosine_similarities = sorted(cosine_similarities.items(), key=lambda x: x[1], reverse=True)
     return sorted_cosine_similarities[0:4]
 
-def generate_embedding(text):
-    response = openai.Embedding.create(
-        input=text,
-        engine="text-embedding-ada-002"
-    )
-    embedding = np.array(response['data'][0]['embedding'])
-    return embedding
+def predict(message, history):
+    # Connect to the database
+    conn = sqlite3.connect('text_chunks_with_embeddings.db')  # Update the database name
+    cursor = conn.cursor()
+    cursor.execute("SELECT text, embedding FROM chunks")
+    rows = cursor.fetchall()
 
-def context_gpt_response(question):
-    vector = generate_embedding(question)
-    match_list = find_closest_neighbors(vector)
+    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()
 
+    match_list = find_closest_neighbors(message, dictionary_of_vectors)
     context = ''
     for match in match_list:
         context += str(match[0])
-    
-    context = context[:1500]  # Limit context to the last 1500 characters
+    context = context[:1500]  # Limit context to 1500 characters
 
-    prep = f"This is an OpenAI model designed to answer questions specific to grant-making applications for an aquarium. Here is some question-specific context: {context}. Q: {question} A: "
-    response = openai.Completion.create(
-        engine="gpt-4",
-        prompt=prep,
-        temperature=0.7,
-        max_tokens=220,
+    prep = f"This is an OpenAI model designed to answer questions specific to grant-making applications for an aquarium. Here is some question-specific context: {context}. 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
     )
 
-    return response['choices'][0]['text']
+    partial_message = ""
+    for chunk in response:
+        if len(chunk['choices'][0]['delta']) != 0:
+            partial_message += chunk['choices'][0]['delta']['content']
+            yield partial_message
+
+gr.ChatInterface(predict).queue().launch()
 
-iface = gr.Interface(fn=context_gpt_response, inputs="text", outputs="text", title="Aquarium Grant Application Chatbot", description="Context-specific chatbot for grant writing", examples=[["What types of projects are eligible for funding?"], ["Tell me more about the application process."], ["What will be the most impactful grant opportunities?"]])
-iface.launch()