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import openai
import sqlite3
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
import gradio as gr
# 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(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)
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 context_gpt_response(question):
vector = generate_embedding(question)
match_list = find_closest_neighbors(vector)
context = ''
for match in match_list:
context += str(match[0])
context = context[:1500] # Limit context to the last 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,
)
return response['choices'][0]['text']
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() |