Delete app.py

app.py

@@ -1,295 +0,0 @@
-import os
-import faiss
-import pickle
-import torch
-
-from sentence_transformers import SentenceTransformer
-from transformers import AutoTokenizer
-from transformers import AutoModelForSeq2SeqLM
-
-
-import gradio as gr
-
-
-# ==================================================
-# CONFIG
-# ==================================================
-
-CONFIG = {
-
-    "retriever_model_path":
-    "swathibp/BGE-base_finetuned",
-
-    "generator_model_path":
-    "swathibp/Flan_T5_merged",
-
-    "save_dir":
-    ".",
-
-    "top_k": 3,
-
-    "max_new_tokens": 250,
-
-    "device":
-    "cuda"
-    if torch.cuda.is_available()
-    else "cpu"
-
-}
-
-os.makedirs(
-    CONFIG["save_dir"],
-    exist_ok=True
-)
-
-print(
-    "DEVICE:",
-    CONFIG["device"]
-)
-
-
-# ==================================================
-# BUILD / LOAD FAISS
-# ==================================================
-
-INDEX_FILE = \
-f"{CONFIG['save_dir']}/index.faiss"
-
-DOC_FILE = \
-f"{CONFIG['save_dir']}/docs.pkl"
-
-
-print("Loading Retriever...")
-
-retriever = SentenceTransformer(
-    CONFIG["retriever_model_path"]
-)
-
-if os.path.exists(INDEX_FILE):
-
-    print("Loading Stored FAISS Index")
-
-    index = faiss.read_index(
-        INDEX_FILE
-    )
-
-    with open(
-        DOC_FILE,
-        "rb"
-    ) as f:
-
-        documents = pickle.load(f)
-
-   
-# ==================================================
-# LOAD GENERATOR
-# ==================================================
-
-print("Loading FLAN Generator...")
-
-tokenizer = \
-AutoTokenizer.from_pretrained(
-
-    CONFIG[
-        "generator_model_path"
-    ]
-
-)
-
-generator = \
-AutoModelForSeq2SeqLM.from_pretrained(
-
-    CONFIG[
-        "generator_model_path"
-    ]
-
-).to(
-    CONFIG["device"]
-)
-
-generator.eval()
-
-print("Generator Loaded")
-
-
-# ==================================================
-# RETRIEVAL
-# ==================================================
-
-def retrieve(query):
-
-    emb = \
-    retriever.encode(
-
-        [query],
-
-        convert_to_numpy=True
-
-    )
-
-    faiss.normalize_L2(
-        emb
-    )
-
-    scores, indices = \
-    index.search(
-
-        emb,
-
-        CONFIG["top_k"]
-
-    )
-
-    docs = []
-
-    for idx in indices[0]:
-
-        docs.append(
-            documents[idx]
-        )
-
-    return docs
-
-
-# ==================================================
-# GENERATION
-# ==================================================
-
-def generate(query):
-
-    docs = retrieve(query)
-
-    instruction = (
-
-        "Answer ONLY using the information provided in the context. "
-
-        "If the answer is not available, reply exactly: "
-
-        "'Not found in the provided documents.'"
-
-    )
-
-    context = "\n".join(
-        docs
-    )
-
-    prompt = f"""
-
-{instruction}
-
-Context:
-
-{context}
-
-Question:
-
-{query}
-
-Answer:
-
-"""
-
-    inputs = tokenizer(
-
-        prompt,
-
-        return_tensors="pt",
-
-        truncation=True
-
-    ).to(
-        CONFIG["device"]
-    )
-
-    with torch.no_grad():
-
-        outputs = \
-        generator.generate(
-
-            **inputs,
-
-            max_new_tokens=
-            CONFIG[
-                "max_new_tokens"
-            ],
-
-            do_sample=False,
-
-            early_stopping=True
-
-        )
-
-    answer = \
-    tokenizer.decode(
-
-        outputs[0],
-
-        skip_special_tokens=True
-
-    )
-
-    return answer, context
-
-
-# ==================================================
-# UI
-# ==================================================
-
-with gr.Blocks() as demo:
-
-    gr.Markdown(
-        "# MAHE QA System"
-    )
-
-    q = gr.Textbox(
-
-        label="Question",
-
-        placeholder=
-        "Enter your MAHE question here...",
-
-        lines=3,
-
-        max_lines=5
-    )
-
-    ask = gr.Button(
-        "Generate Answer"
-    )
-
-    ans = gr.Textbox(
-
-        label="Answer",
-
-        lines=15,
-
-        max_lines=30,
-
-       #show_copy_button=True
-    )
-
-    ctx = gr.Textbox(
-
-        label="Retrieved Context",
-
-        lines=20,
-
-        max_lines=40,
-
-        show_copy_button=True
-    )
-
-    ask.click(
-
-        generate,
-
-        q,
-
-        [ans, ctx]
-
-    )
-
-demo.launch(
-    share=True,
-    debug=True
-)