Delete app.py

app.py

@@ -1,90 +0,0 @@
-from transformers import AutoModel
-import torch
-import transformers
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from sklearn.metrics.pairwise import cosine_similarity
-from sentence_transformers import SentenceTransformer
-
-import gdown
-import json
-import textwrap
-import warnings
-import openai
-import pandas
-import gradio as gr
-
-warnings.filterwarnings("ignore")
-
-openai.api_key = "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"
-
-
-def generate_prompt(question):
-  prompt = f"""
-### <instruction>: Given an suitable answer for the question asked.
-### <human>: {question}
-### <assistant>:
-  """.strip()
-  return prompt
-
-file_id = '1CjJ-CQhZyr8QowwSksw5uo7O9OYgbq96'
-
-url = f'https://drive.google.com/uc?id={file_id}'
-
-output_file = 'data.xlsx'
-
-gdown.download(url, output_file, quiet=False)
-
-df = pd.read_csv(output_file, encoding='latin-1')
-
-df.head()
-
-sentences = []
-for row in df['QUESTION']:
-    sentences.append(row)
-
-
-model_encode = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')
-embeddings = model_encode.encode(sentences)
-
-answer = []
-for index, val in enumerate(df['ORIGINAL/SYNONYM']):
-    if str(val) == "Original":
-        answer.append(index)
-
-def answer_prompt(text):
-
-  ind, sim = 0, 0
-  bot_response = ''
-  text_embedding = model_encode.encode(text)
-  for index, val in enumerate(embeddings):
-    res = cosine_similarity(text_embedding.reshape(1,-1),embeddings[index].reshape(1,-1))
-    if res[0][0] > sim:
-      sim = res[0][0]
-      ind = index
-
-  for i in range(len(answer)):
-    if answer[i] > ind:
-      bot_response =  bot_response =  f'Similarity Score is {round(sim * 100)} %' + '\n\n' + f'The issue is raised for {df["TECHNOLOGY"][answer[i - 1]]}' + '\n\n' + df['SOLUTION'][answer[i - 1]]
-      break
-
-  if sim > 0.5:
-    return bot_response
-
-  else:
-
-    prompt = generate_prompt(text)
-    response = openai.Completion.create(
-                     engine="gpt-3.5-turbo-instruct",
-                     prompt = prompt,
-                     max_tokens = 1024,
-                     top_p = 0.7,
-                     temperature = 0.3,
-                     presence_penalty = 0.7,
-                  )
-
-    return 'This response is generated by our LLM' + '\n' + response['choices'][0]['text']
-
-iface = gr.Interface(fn=answer_prompt, inputs="text",
-                                       outputs="text")
-
-iface.launch(inline=False)