add app

app.py

@@ -0,0 +1,131 @@
+# -*- coding: utf-8 -*-
+"""Mockinterview-Falcon.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1hCKPV5U_bg7QQXPUIwwDvCdB1N8fgz0z
+
+## Install dependencies
+"""
+
+
+"""## Import dependencies"""
+
+import os
+import torch
+from transformers import (
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig,
+    pipeline
+)
+
+import csv
+import codecs
+
+from langchain import PromptTemplate,  LLMChain
+import random
+import json
+
+"""## Creating pipeline for Falcon-7b"""
+
+from langchain import HuggingFacePipeline
+from transformers import AutoTokenizer, pipeline
+import torch
+
+model = "tiiuae/falcon-7b-instruct" #tiiuae/falcon-40b-instruct
+
+tokenizer = AutoTokenizer.from_pretrained(model)
+
+pipeline = pipeline(
+    "text-generation", #task
+    model=model,
+    tokenizer=tokenizer,
+    torch_dtype=torch.bfloat16,
+    trust_remote_code=True,
+    device_map="auto",
+    max_length=512,
+    do_sample=True,
+    top_k=10,
+    num_return_sequences=1,
+    eos_token_id=tokenizer.eos_token_id
+)
+
+llm = HuggingFacePipeline(pipeline = pipeline, model_kwargs = {'temperature':0})
+
+"""## Loading csv (attempting the program without RAG)
+
+* RAG was reducing program efficiency.
+"""
+
+file = "/content/Combined_Data_set.csv"
+
+fields = []
+rows = []
+
+with codecs.open(file, 'r', 'utf-8') as csvfile:
+  csvreader = csv.reader(csvfile)
+  fields = next(csvreader)
+
+  for row in csvreader:
+    rows.append(row)
+
+"""## LLMChain for deciding next question"""
+
+# Here we can make certain changes through the prompt template, like the tone in which we want the questions to be asked, we may use few shots for that.
+
+record = random.randint(0,len(rows))
+
+def get_question():
+    topic = rows[record][0] #extracting question from csv
+
+    template1 = """
+    You are a data science interviewer between an interview, ask a question regarding the following given topic:
+    Topic to ask question on as a interviewer: {question}
+    """
+    prompt1 = PromptTemplate(template=template1, input_variables=["question"])
+
+    llm_chain = LLMChain(prompt=prompt1, llm=llm)
+
+    next_question = llm_chain.run(topic)
+    # print(next_question)
+    json_string = "{{\"question\": \"{}\"}}".format(next_question)
+
+    json_ques = json.loads(json_string, strict=False)
+
+    return json_ques
+
+result = get_question()
+result
+
+result['question']
+
+"""## LLMChain for evaluating user response"""
+
+# Now we can improve performance through the prompt, like we can provide a few shots, tell it about how to give positive and negative responses using some shots.
+
+# corr_ans = rows[record][1] #extracting answer from csv
+
+def get_evaluation(response):
+    template2 = '''
+    You are a data scientist interviewer and you are taking the interview of someone.
+    Evaluate the response given by that person: {response}
+
+    '''
+    prompt2 = PromptTemplate(template=template2, input_variables=["response"])
+
+    llm_chain2 = LLMChain(prompt=prompt2, llm=llm)
+
+    evaluation = llm_chain2.run(response)
+    #print(evaluation)
+    json_string = "{{\"response\" : \"{}\" }}".format(evaluation)
+
+    json_eval = json.loads(json_string, strict=False)
+    return json_eval
+
+response = input("Enter your response: ")
+result = get_evaluation(response)
+result
+
+result['response']

requirements.txt

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+transformers 
+einops
+accelerate 
+langchain 
+bitsandbytes
+uvicorn