app.py

import openai
import sqlite3
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
import os
import gradio as gr
from docx import Document
from PyPDF2 import PdfFileReader
import re
from gradio import Interface, components


# Set OpenAI API key from environment variable
openai.api_key = os.environ["Secret"]

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)

    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 extract_words_from_docx(filename):
    doc = Document(filename)
    full_text = []
    for paragraph in doc.paragraphs:
        full_text.append(paragraph.text)
    text = '\n'.join(full_text)
    return re.findall(r'\b\w+\b', text)

def extract_words_from_pdf(filename):
    with open(filename, "rb") as file:
        pdf = PdfFileReader(file)
        text = ""
        for page_num in range(pdf.getNumPages()):
            text += pdf.getPage(page_num).extractText()
    return re.findall(r'\b\w+\b', text)

def process_file(file_obj):
    if file_obj is not None:
        # Determine file type
        if file_obj.name.endswith('.docx'):
            words = extract_words_from_docx(file_obj.name)
        elif file_obj.name.endswith('.pdf'):
            words = extract_words_from_pdf(file_obj.name)
        else:
            return "Unsupported file type."

        # Chunk the words into 200-word chunks and add to database
        conn = sqlite3.connect('text_chunks_with_embeddings (1).db')
        cursor = conn.cursor()

        chunks = [" ".join(words[i:i+200]) for i in range(0, len(words), 200)]
        for chunk in chunks:
            embedding = openai.Embedding.create(input=chunk, engine="text-embedding-ada-002")['data'][0]['embedding']
            embedding_str = " ".join(map(str, embedding))
            cursor.execute("INSERT INTO chunks (text, embedding) VALUES (?, ?)", (chunk, embedding_str))

        conn.commit()
        conn.close()
        return "File processed and added to database."

    return "No file uploaded."

def predict(message, history, file_obj=None):
    # If there's a file, process it first
    if file_obj:
        process_file(file_obj)

    # Connect to the database
    conn = sqlite3.connect('text_chunks_with_embeddings (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()

    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 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: {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 += chunk['choices'][0]['delta']['content']
            yield partial_message

# Modify the Gradio interface to include the file upload component
Interface(fn=predict, 
          inputs=["text", "list", components.File(label="Upload PDF or DOCX file")], 
          outputs="textbox",  # Using "textbox" as a replacement for "chat"
          live=True).launch()