Create app.py

app.py

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+import torch
+import transformers
+import pandas as pd
+import numpy as np
+import gradio as gr
+
+from torch import cuda, bfloat16
+from langchain.llms import HuggingFacePipeline
+from langchain.document_loaders.csv_loader import CSVLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chains import ConversationalRetrievalChain
+
+model_id = 'meta-llama/Llama-2-7b-chat-hf'
+device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'
+bnb_config = transformers.BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_quant_type='nf4',
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_compute_dtype=bfloat16
+)
+hf_auth = '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'
+model_config = transformers.AutoConfig.from_pretrained(
+    model_id,
+    use_auth_token=hf_auth
+)
+model = transformers.AutoModelForCausalLM.from_pretrained(
+    model_id,
+    trust_remote_code=True,
+    config=model_config,
+    quantization_config=bnb_config,
+    device_map='auto',
+    use_auth_token=hf_auth
+)
+model.eval()
+tokenizer = transformers.AutoTokenizer.from_pretrained(
+    model_id,
+    use_auth_token=hf_auth
+)
+stop_list = ['\nHuman:', '\n```\n']
+stop_token_ids = [tokenizer(x)['input_ids'] for x in stop_list]
+stop_token_ids = [torch.LongTensor(x).to(device) for x in stop_token_ids]
+class StopOnTokens(StoppingCriteria):
+    def __call__(self, input_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
+        for stop_ids in stop_token_ids:
+            if torch.eq(input_ids[0][-len(stop_ids):], stop_ids).all():
+                return True
+        return False
+stopping_criteria = StoppingCriteriaList([StopOnTokens()])
+generate_text = transformers.pipeline(
+    model=model,
+    tokenizer=tokenizer,
+    return_full_text=True,
+    task='text-generation',
+    stopping_criteria=stopping_criteria,
+    temperature=0.1,
+    max_new_tokens=512,
+    repetition_penalty=1.1
+)
+llm = HuggingFacePipeline(pipeline=generate_text)
+data = pd.read_json('interviewQna.json')
+data.to_csv('interviewQna.csv', index=False)
+df = pd.read_csv('interviewQna.csv')
+df.to_csv("output.csv", index=False)
+loader = CSVLoader(file_path='interviewQna.csv')
+document = loader.load()
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=20)
+all_splits = text_splitter.split_documents(document)
+model_name = "sentence-transformers/all-mpnet-base-v2"
+model_kwargs = {"device": "cuda"}
+embeddings = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)
+vectorstore = FAISS.from_documents(all_splits, embeddings)
+chain = ConversationalRetrievalChain.from_llm(llm, vectorstore.as_retriever(), return_source_documents=True)
+
+def interview_evaluator(question):
+    chat_history = []
+    result = chain({"question": question, "chat_history": chat_history})
+    return result['answer']
+
+iface = gr.Interface(
+    fn=interview_evaluator,
+    inputs=gr.Textbox(lines=2,label="Question", placeholder="Enter Question Here:"),
+    outputs=gr.Textbox(label="Answer"),
+    title= "CyberSage"
+)
+
+if __name__ == "__main__":
+    iface.launch()