Update app.py

app.py

@@ -1,78 +1,91 @@
-import fitz  # PyMuPDF
+import fitz  # PyMuPDF for PDF extraction
 import re
-from datasets import Dataset
-from transformers import AutoModelForCausalLM, TrainingArguments, Trainer
-import gradio as gr
-from transformers import pipeline
-
 
 def extract_text_from_pdf(pdf_path):
     """Extract text from a PDF file"""
     doc = fitz.open(pdf_path)
-    text = ""
-    for page in doc:
-        text += page.get_text("text") + "\n"
-    return text
-
-pdf_text = extract_text_from_pdf("new-american-standard-bible.pdf")
-#print(pdf_text[:1000])  # Preview first 1000 characters
+    text = "\n".join([page.get_text("text") for page in doc])
+    return text.strip()
 
 def preprocess_text(text):
-    """Clean and preprocess text"""
-    text = re.sub(r'\s+', ' ', text)  # Remove extra whitespace
-    text = text.strip()
-    return text
+    """Basic text preprocessing"""
+    return re.sub(r"\s+", " ", text).strip()
 
+pdf_text = extract_text_from_pdf("your_document.pdf")
 clean_text = preprocess_text(pdf_text)
-#print(clean_text[:1000])  # Preview cleaned text
 
-# Create a dataset from text
-data = {"text": [clean_text]}  # Single text entry
-dataset = Dataset.from_dict(data)
-
-# Tokenize text
+from datasets import Dataset
 from transformers import AutoTokenizer
 
-model_name = "distilbert-base-uncased"
+model_name = "meta-llama/Llama-2-7b-hf"  # You can use a smaller one like "meta-llama/Llama-2-7b-chat-hf"
+
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 
+# Create dataset
+data = {"text": [clean_text]}
+dataset = Dataset.from_dict(data)
+
+# Tokenization function
 def tokenize_function(examples):
     tokens = tokenizer(examples["text"], truncation=True, padding="max_length", max_length=512)
-    tokens["labels"] = tokens["input_ids"].copy() # Use input as labels for unsupervised learning
+    tokens["labels"] = tokens["input_ids"].copy()  # Use input as labels for text generation
     return tokens
 
 tokenized_datasets = dataset.map(tokenize_function, batched=True)
 
-model = AutoModelForCausalLM.from_pretrained(model_name)  # Adjust for task
+from transformers import AutoModelForCausalLM, TrainingArguments, Trainer
+from peft import LoraConfig, get_peft_model
+
+# Load LLaMA 2 model in 4-bit mode to save memory
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    load_in_4bit=True,  # Use 4-bit quantization for efficiency
+    device_map="auto"
+)
+
+# Apply LoRA (efficient fine-tuning)
+lora_config = LoraConfig(
+    r=8,  # Low-rank parameter
+    lora_alpha=32,
+    target_modules=["q_proj", "v_proj"],  # Applies only to attention layers
+    lora_dropout=0.05
+)
+
+model = get_peft_model(model, lora_config)
 
 training_args = TrainingArguments(
     output_dir="./results",
     evaluation_strategy="epoch",
-    learning_rate=2e-5,
-    per_device_train_batch_size=8,
-    per_device_eval_batch_size=8,
+    learning_rate=2e-4,
+    per_device_train_batch_size=1,  # Reduce batch size for memory efficiency
+    per_device_eval_batch_size=1,
     num_train_epochs=3,
     weight_decay=0.01,
     save_strategy="epoch",
+    logging_dir="./logs",
+    logging_steps=10,
 )
 
 trainer = Trainer(
     model=model,
     args=training_args,
     train_dataset=tokenized_datasets,
-    eval_dataset=tokenized_datasets,
     tokenizer=tokenizer,
 )
 
 trainer.train()
 
-model.save_pretrained("./distilbert-base-uncased-fine_tuned_model-NASB")
-tokenizer.save_pretrained("./distilbert-base-uncased-fine_tuned_model-NASB")
+model.save_pretrained("./fine_tuned_llama2")
+tokenizer.save_pretrained("./fine_tuned_llama2")
+
+import gradio as gr
+from transformers import pipeline
 
-classifier = pipeline("text-classification", model="./distilbert-base-uncased-fine_tuned_model-NASB")
+chatbot = pipeline("text-generation", model="./fine_tuned_llama2")
 
-def chatbot_response(text):
-    return classifier(text)
+def chatbot_response(prompt):
+    result = chatbot(prompt, max_length=100, do_sample=True, temperature=0.7)
+    return result[0]["generated_text"]
 
 iface = gr.Interface(fn=chatbot_response, inputs="text", outputs="text")
 iface.launch()