Create app.py

app.py

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+import gradio as gr
+from gradio.components import Textbox, Checkbox
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, T5ForConditionalGeneration
+from peft import PeftModel
+import torch
+import datasets
+from sentence_transformers import CrossEncoder
+import math
+import re
+from nltk import sent_tokenize, word_tokenize
+import nltk
+nltk.download('punkt')
+
+# Load bi encoder
+top_k = 10
+cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-6-v2')
+
+# Load your fine-tuned model and tokenizer
+model_name = "google/flan-t5-large"
+peft_name = "legacy107/flan-t5-large-ia3-newsqa"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+pretrained_model = T5ForConditionalGeneration.from_pretrained(model_name)
+model = T5ForConditionalGeneration.from_pretrained(model_name)
+model = PeftModel.from_pretrained(model, peft_name)
+max_length = 512
+max_target_length = 200
+
+# Load your dataset
+dataset = datasets.load_dataset("legacy107/newsqa", split="test")
+dataset = dataset.shuffle()
+dataset = dataset.select(range(10))
+
+# Context chunking
+def chunk_splitter(context, chunk_size=50, overlap=0.10):
+    overlap_size = chunk_size * overlap
+    sentences = nltk.sent_tokenize(context)
+
+    chunks = []
+    text = sentences[0]
+
+    if len(sentences) == 1:
+        chunks.append(text)
+
+    i = 1
+    while i < len(sentences):
+        text += " " + sentences[i]
+        i += 1
+        while i < len(sentences) and len(nltk.word_tokenize(f"{text} {sentences[i]}")) <= chunk_size:
+            text += " " + sentences[i]
+            i += 1
+
+        text = text.replace('\"','"').replace("\'","'").replace('\n\n\n'," ").replace('\n\n'," ").replace('\n'," ")
+        chunks.append(text)
+
+        if (i >= len(sentences)):
+            break
+
+        j = i - 1
+        text = sentences[j]
+        while j >= 0 and len(nltk.word_tokenize(f"{sentences[j]} {text}")) <= overlap_size:
+            text = sentences[j] + " " + text
+            j -= 1
+
+    return chunks
+
+
+def retrieve_context(query, contexts):
+    hits = [{"corpus_id": i} for i in range(len(contexts))]
+    cross_inp = [[query, contexts[hit["corpus_id"]]] for hit in hits]
+    cross_scores = cross_encoder.predict(cross_inp, show_progress_bar=False)
+
+    for idx in range(len(cross_scores)):
+        hits[idx]["cross-score"] = cross_scores[idx]
+
+    hits = sorted(hits, key=lambda x: x["cross-score"], reverse=True)
+
+    return " ".join(
+        [contexts[hit["corpus_id"]] for hit in hits[0:top_k]]
+    ).replace("\n", " ")
+
+
+# Define your function to generate answers
+def generate_answer(question, context, ground, do_pretrained):
+    contexts = chunk_splitter(context)
+    context = retrieve_context(question, contexts)
+    
+    # Combine question and context
+    input_text = f"question: {question} context: {context}"
+
+    # Tokenize the input text
+    input_ids = tokenizer(
+        input_text,
+        return_tensors="pt",
+        padding="max_length",
+        truncation=True,
+        max_length=max_length,
+    ).input_ids
+
+    # Generate the answer
+    with torch.no_grad():
+        generated_ids = model.generate(input_ids=input_ids, max_new_tokens=max_target_length)
+
+    # Decode and return the generated answer
+    generated_answer = tokenizer.decode(generated_ids[0], skip_special_tokens=True)
+
+    # Get pretrained model's answer
+    pretrained_answer = ""
+    if do_pretrained:
+        with torch.no_grad():
+            pretrained_generated_ids = pretrained_model.generate(input_ids=input_ids, max_new_tokens=max_target_length)
+            pretrained_answer = tokenizer.decode(pretrained_generated_ids[0], skip_special_tokens=True)
+
+    return generated_answer, context, pretrained_answer
+
+
+# Define a function to list examples from the dataset
+def list_examples():
+    examples = []
+    for example in dataset:
+        context = example["context"]
+        question = example["question"]
+        answer = " | ".join(example["answers"])
+        examples.append([question, context, answer, True])
+    return examples
+
+
+# Create a Gradio interface
+iface = gr.Interface(
+    fn=generate_answer,
+    inputs=[
+        Textbox(label="Question"),
+        Textbox(label="Context"),
+        Textbox(label="Ground truth"),
+        Checkbox(label="Include pretrained model's result")
+    ],
+    outputs=[
+        Textbox(label="Generated Answer"),
+        Textbox(label="Retrieved Context"),
+        Textbox(label="Pretrained Model's Answer")
+    ],
+    examples=list_examples(),
+    examples_per_page=1
+)
+
+# Launch the Gradio interface
+iface.launch()