Create app.py

app.py

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+# Install necessary libraries
+!pip install PyPDF2 transformers torch accelerate
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+import PyPDF2
+
+# Step 1: Extract text from PDF
+def extract_text_from_pdf(pdf_path):
+    pdf_text = ""
+    with open(pdf_path, "rb") as file:
+        reader = PyPDF2.PdfReader(file)
+        for page_num in range(len(reader.pages)):
+            page = reader.pages[page_num]
+            pdf_text += page.extract_text()
+    return pdf_text
+
+# Step 2: Initialize the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("ricepaper/vi-gemma-2b-RAG")
+model = AutoModelForCausalLM.from_pretrained(
+    "ricepaper/vi-gemma-2b-RAG",
+    device_map="auto",
+    torch_dtype=torch.bfloat16
+)
+
+# Use GPU if available
+if torch.cuda.is_available():
+    model.to("cuda")
+
+# Step 3: Define the prompt format for the model
+prompt = """
+### Instruction and Input:
+Based on the following context/document:
+{}
+Please answer the question: {}
+
+### Response:
+{}
+"""
+
+# Step 4: Function to generate answer based on query and context
+def generate_answer(context, query):
+    input_text = prompt.format(context, query, "")
+    input_ids = tokenizer(input_text, return_tensors="pt")
+
+    # Use GPU for input ids if available
+    if torch.cuda.is_available():
+        input_ids = input_ids.to("cuda")
+
+    # Generate text using the model
+    outputs = model.generate(
+        **input_ids,
+        max_new_tokens=500,
+        no_repeat_ngram_size=5,
+    )
+
+    # Decode and print the results
+    answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return answer
+
+# Step 5: Read PDF and generate answer
+pdf_path = '/content/monopoly.pdf'  # Replace with your PDF file path
+pdf_text = extract_text_from_pdf(pdf_path)
+query = "Your question here"  # Replace with your question
+
+# Generate answer based on extracted PDF text and the query
+answer = generate_answer(pdf_text, query)
+print("Answer:", answer)