Upload app.py

app.py

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+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer, GenerationConfig, BitsAndBytesConfig
+import gradio as gr
+
+# Authenticate using token from environment
+hf_token = os.getenv("HF_TOKEN")
+login(token=hf_token)
+
+# Use quantization for low-memory GPU inference
+quantization_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.bfloat16,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4"
+)
+
+model_name = "mistralai/Mistral-7B-Instruct-v0.3"
+
+# Load model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    quantization_config=quantization_config,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# Define generation function
+def generate_qa(text):
+    prompt = f"""### Instruction:
+Based on the following SAP Note, generate exactly 20 unique and informative question-answer pairs.
+Each question must refer to the SAP note number from text if additional context is needed.
+Only output the pairs in the format:
+Q1: ...
+A1: ...
+...
+Q20: ...
+A20: ...
+
+### Input:
+{text}
+
+### Response:
+"""
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    outputs = model.generate(
+        input_ids=inputs.input_ids,
+        attention_mask=inputs.attention_mask,
+        max_new_tokens=2500,
+        do_sample=True,
+        temperature=0.9,
+        top_p=0.95,
+        repetition_penalty=1.1,
+        pad_token_id=tokenizer.eos_token_id
+    )
+
+    output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    qa_pairs = output_text.split("### Response:")[-1].strip()
+    return qa_pairs
+
+# Define Gradio UI
+demo = gr.Interface(
+    fn=generate_qa,
+    inputs=gr.Textbox(lines=20, label="SAP Note Text"),
+    outputs=gr.Textbox(lines=25, label="Generated Q&A Pairs"),
+    title="Mistral Q&A Generator for SAP Notes",
+    description="Upload or paste SAP Note content to generate 20 question-answer pairs."
+)
+
+demo.launch()