Delete app.py

app.py

@@ -1,132 +0,0 @@
-# from fastapi import FastAPI, Request
-# from pydantic import BaseModel
-# # from unsloth import FastLanguageModel
-# import torch
-# import re
-
-
-
-
-# app = FastAPI()
-
-# # Load model once on startup
-# model, tokenizer = FastLanguageModel.from_pretrained(
-#     model_name = "unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
-#     max_seq_length = 2048,
-#     dtype = None,
-#     load_in_4bit = True,
-# )
-# FastLanguageModel.for_inference(model)
-
-# class SAPNoteRequest(BaseModel):
-#     text: str
-
-# @app.post("/generate_qa")
-# def generate_qa(req: SAPNoteRequest):
-#     text = req.text
-#     match = re.search(r"SAP Note\s*(\d+)", text)
-#     sap_note_number = match.group(1) if match else "UNKNOWN"
-
-#     prompt = f"""
-#     Generate 20 question-answer pairs based on the following SAP Note.
-#     Each question should include the SAP note number {sap_note_number} to clarify context.
-
-#     \"\"\"{text}\"\"\"
-
-#     Q1: question
-#     A1: answer
-
-#     ### Response:
-#     """
-#     inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
-#     outputs = model.generate(
-#         inputs.input_ids,
-#         max_new_tokens=2048,
-#         do_sample=True,
-#         temperature=0.7,
-#         top_p=0.95,
-#         repetition_penalty=1.2
-#     )
-#     output_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-#     qa_pairs = output_text.split("### Response:")[-1].strip()
-#     return {"qa_pairs": qa_pairs}
-
-
-### Hugging face code
-
-# import torch
-# from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
-
-# # Quantization settings
-# quantization_config = BitsAndBytesConfig(
-#     load_in_4bit=True,
-#     bnb_4bit_quant_type="nf4",
-#     bnb_4bit_compute_dtype=torch.float16,
-# )
-
-import torch
-from transformers import AutoTokenizer, AutoModelForCausalLM, TextStreamer, GenerationConfig, BitsAndBytesConfig
-import gradio as gr
-
-# 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()