Update app.py

app.py

@@ -1,11 +1,105 @@
+import torch
+from transformers import AutoTokenizer, AutoModel, AutoModelForSeq2SeqLM
 import gradio as gr
-import constants
+import faiss
+import numpy as np
+import pandas as pd
 from transformers import pipeline
+from sentence_transformers import SentenceTransformer
 
-question_answerer = pipeline("question-answering", model=constants.MODEL_NAME)
+model_name = "9wimu9/mt5-xl-sin-odqa-1"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForSeq2SeqLM.from_pretrained(model_name)
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+question_answerer_seq_2seq = model.to(device)
+
+retriever_model = SentenceTransformer('9wimu9/retriever-model-sinhala-v2')
+question_answerer = pipeline("question-answering", model='9wimu9/xlm-roberta-large-en-si-only-finetuned-sinquad-v12')
+
+def srq2seq_find_answer(query,context):
+    conditioned_doc = "<P> " + " <P> ".join([d for d in context])
+    query_and_docs = "question: {} context: {}".format(query, conditioned_doc)
+
+    model_input = tokenizer(query_and_docs, truncation=True, padding=True, return_tensors="pt")
+
+    generated_answers_encoded = model.generate(input_ids=model_input["input_ids"].to(device),
+                                           attention_mask=model_input["attention_mask"].to(device),
+                                           min_length=2,
+                                           max_length=120,
+                                           early_stopping=True,
+                                           num_beams=9,
+                                           temperature=0.9,
+                                           do_sample=False,
+                                           top_k=None,
+                                           top_p=None,
+                                           eos_token_id=tokenizer.eos_token_id,
+                                           no_repeat_ngram_size=8,
+                                           num_return_sequences=1)
+    return tokenizer.batch_decode(generated_answers_encoded, skip_special_tokens=True,clean_up_tokenization_spaces=True)[0]
+
+def encode_file(file_path):
+  passages = []
+  with open(file_path) as file:
+    for item in file:
+      passages.append([item])
+  df = pd.DataFrame(passages, columns = ['text'])
+  text = df['text']
+  vectors = retriever_model.encode(text)
+  return vectors,passages
+
+def upload_file(files):
+  global index
+  global passages
+  file_paths = [file.name for file in files]
+  vectors,passages = encode_file(file_paths[0])
+  vector_dimension = vectors.shape[1]
+  index = faiss.IndexFlatL2(vector_dimension)
+  faiss.normalize_L2(vectors)
+  index.add(vectors)
+  return file_paths
+
+def question_answer(search_text):
+  search_vector = retriever_model.encode(search_text)
+  print(search_vector)
+  _vector = np.array([search_vector])
+  faiss.normalize_L2(_vector)
+  k = index.ntotal
+  distances, ann = index.search(_vector, k=k)
+  context = passages[ann[0][0]][0]
+  result = question_answerer(question=search_text,     context=context)
+  print(result)
+  return result['answer']
+
+
+def question_answer_generated(search_text):
+  search_vector = retriever_model.encode(search_text)
+  print(search_vector)
+  _vector = np.array([search_vector])
+  faiss.normalize_L2(_vector)
+  k = index.ntotal
+  distances, ann = index.search(_vector, k=k)
+  context = passages[ann[0][0]][0]
+  return srq2seq_find_answer(search_text,[context])
+
+with gr.Blocks() as demo:
+  with gr.Row():
+    with gr.Column():
+      file_output = gr.File()
+      upload_button = gr.UploadButton("Click to Upload a File", file_types=["txt"], file_count="1")
+      upload_button.upload(upload_file, upload_button, file_output)
+  with gr.Row():
+    with gr.Column():
+      name = gr.Textbox(label="question")
+      output = gr.Textbox(label="answer")
+      greet_btn = gr.Button("get Answer - extraction QA")
+      greet_btn.click(fn=question_answer, inputs=name, outputs=output, api_name="greet")
+  with gr.Row():
+    with gr.Column():
+      name = gr.Textbox(label="question")
+      output = gr.Textbox(label="answer")
+      greet_btn = gr.Button("get Answer - Generated QA")
+      greet_btn.click(fn=question_answer_generated, inputs=name, outputs=output, api_name="greet")
+
+demo.launch(debug=True)
 
-def question_answer(context, question):
-    result = question_answerer(question=question, context=context)
-    return result['answer'],result['score']
 
-gr.Interface(fn=question_answer, inputs=["text", "text"], outputs=[gr.components.Textbox(label="Answer"), gr.components.Textbox(label="Probability")]).launch()