Create app.py

app.py

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+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
+
+# 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
+gr.Interface(fn=predict, 
+             inputs=["text", "list", gr.inputs.File(label="Upload PDF or DOCX file (optional)")], 
+             outputs="chat", 
+             live=True).launch()
+
+