Create app.py

app.py

@@ -0,0 +1,107 @@
+import os
+import pandas as pd
+import transformers
+import torch
+from transformers import Trainer, TrainingArguments, DataCollatorForLanguageModeling, AutoTokenizer, AutoModelForCausalLM
+from torch.utils.data import Dataset
+import streamlit as st
+
+# Path to the directory where Ollama stores models
+model_dir = "C:/Users/myuser/.ollama/models/meta-llama-3-8b"
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_dir)
+model = AutoModelForCausalLM.from_pretrained(model_dir)
+
+# Add a padding token to the tokenizer if it doesn't have one
+if tokenizer.pad_token is None:
+    tokenizer.add_special_tokens({'pad_token': '[PAD]'})
+
+# Custom Dataset class
+class CustomTextDataset(Dataset):
+    def __init__(self, tokenized_inputs):
+        self.input_ids = tokenized_inputs['input_ids']
+        self.attention_masks = tokenized_inputs['attention_mask']
+
+    def __len__(self):
+        return len(self.input_ids)
+
+    def __getitem__(self, idx):
+        return {
+            'input_ids': self.input_ids[idx],
+            'attention_mask': self.attention_masks[idx]
+        }
+
+# Prepare dataset function
+def prepare_dataset(texts, tokenizer, block_size=128):
+    inputs = tokenizer(texts, return_tensors='pt', max_length=block_size, truncation=True, padding=True)
+    dataset = CustomTextDataset(inputs)
+    return dataset
+
+# Load the dataset
+file_path = "path/to/it_support_transcript_dataset.csv"
+df = pd.read_csv(file_path)
+
+# Filter the dataset based on the given criteria and make a copy
+filtered_df = df[
+    (df['Resolution Status'] == 'Resolved') &
+    (df['Customer Satisfaction (CSAT) Score'] >= 4) &
+    (df['Customer Feedback Comments'].isin(['Very satisfied', 'Satisfied']))
+].copy()
+
+# Combine only the interaction notes into a single text for training
+filtered_df.loc[:, 'training_text'] = filtered_df['Interaction Notes']
+
+# Select the training text
+training_texts = filtered_df['training_text'].tolist()
+
+# Create CustomTextDataset for fine-tuning
+train_dataset = prepare_dataset(training_texts, tokenizer)
+
+# Data collator for language modeling
+data_collator = DataCollatorForLanguageModeling(
+    tokenizer=tokenizer,
+    mlm=False,
+)
+
+# Training arguments
+training_args = TrainingArguments(
+    output_dir="./results",
+    overwrite_output_dir=True,
+    num_train_epochs=3,
+    per_device_train_batch_size=4,
+    save_steps=10_000,
+    save_total_limit=2,
+)
+
+# Trainer
+trainer = Trainer(
+    model=model,
+    args=training_args,
+    data_collator=data_collator,
+    train_dataset=train_dataset,
+)
+
+# Fine-tune the model
+trainer.train()
+
+# Streamlit app
+st.title("IT Support Assistant")
+
+# Create a text generation pipeline
+text_gen_pipeline = transformers.pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer
+)
+
+def generate_response(input_text):
+    outputs = text_gen_pipeline(input_text, max_length=150, num_return_sequences=1)
+    response = outputs[0]['generated_text']
+    return response
+
+input_text = st.text_input("Enter your IT support query:")
+
+if st.button("Generate Response"):
+    response = generate_response(input_text)
+    st.write("Response:", response)