Create app.py

app.py

@@ -0,0 +1,67 @@
+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()