Create app.py

app.py

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+import pandas as pd
+import numpy as np
+import gradio as gr
+from transformers import GPT2LMHeadModel, GPT2Tokenizer, AutoTokenizer, AutoModel
+import torch
+from sentence_transformers import SentenceTransformer, util
+from tqdm import tqdm
+from peewee import SqliteDatabase, Model, TextField
+
+# Load the Enron Email Dataset
+emails_df = pd.read_csv("/content/emails.csv")
+
+# Define the ChromaDB database
+db = SqliteDatabase('email_embeddings.db')
+
+# Define the model and tokenizer
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+model = GPT2LMHeadModel.from_pretrained("gpt2")
+
+# Fine-tune the Language Model on the dataset
+# Fine-tune the Language Model on the dataset
+# Tokenize the dataset
+tokenized_texts = [tokenizer.encode(text, return_tensors="pt", max_length=512) for text in emails_df['Message']]
+
+# Convert tokenized texts to tensors
+input_ids = torch.cat(tokenized_texts, dim=1)
+
+# Define a PyTorch dataset
+dataset = torch.utils.data.TensorDataset(input_ids)
+
+
+# Define the Sentence Transformer model
+sentence_model = SentenceTransformer('distilbert-base-nli-mean-tokens')
+
+# Function to create embeddings of the email dataset and store them in the ChromaDB database
+def create_embeddings():
+    db.connect()
+    db.create_tables([Email])
+
+    embeddings = []
+
+    for index, row in tqdm(emails_df.iterrows(), total=len(emails_df)):
+        text = row['Message']
+        embeddings.append(sentence_model.encode(text))
+
+    for index, embedding in enumerate(embeddings):
+        Email.create(id=index, embedding=embedding.tobytes())
+
+    db.close()
+
+# Define the Gradio Interface
+def answer_question(question):
+    # Encode the question
+    inputs = tokenizer(question, return_tensors="pt", max_length=512, truncation=True)
+
+    # Generate response using the model
+    outputs = model.generate(**inputs)
+
+    # Decode the response
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    return response
+
+# Define the Peewee Model for the ChromaDB database
+class BaseModel(Model):
+    class Meta:
+        database = db
+
+class Email(BaseModel):
+    embedding = TextField()
+
+# Create a Gradio Interface
+gr.Interface(fn=answer_question, inputs="text", outputs="text").launch()
+
+# Uncomment the line below to create embeddings and store them in the ChromaDB database
+create_embeddings()