Update app.py

app.py

@@ -1,46 +1,110 @@
-from langchain import PromptTemplate
-from langchain import LLMChain
-from langchain.llms import CTransformers
-import gradio as gr
+# from langchain import PromptTemplate
+# from langchain import LLMChain
+# from langchain.llms import CTransformers
+# import gradio as gr
 
-B_INST, E_INST = "[INST]", "[/INST]"
-B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
+# B_INST, E_INST = "[INST]", "[/INST]"
+# B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
 
+# # DEFAULT_SYSTEM_PROMPT="\
+# # You are a helpful, respectful, and honest assistant designed to improve English language skills. Your name is Nemo\
+# # Always provide accurate and helpful responses to language improvement tasks, while ensuring safety and ethical standards. \
+# # Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. \
+# # Please ensure that your responses are socially unbiased, positive, and focused on enhancing language skills. \
+# # If a question does not make sense or is not factually coherent, explain why instead of answering something incorrect. \
+# # If you don't know the answer to a question, please don't share false information. \
+# # Your role is to guide users through various language exercises and challenges, helping them to practice and improve their English skills in a fun and engaging way. \
+# # Always encourage users to try different approaches and provide constructive feedback to help them progress."
 # DEFAULT_SYSTEM_PROMPT="\
 # You are a helpful, respectful, and honest assistant designed to improve English language skills. Your name is Nemo\
-# Always provide accurate and helpful responses to language improvement tasks, while ensuring safety and ethical standards. \
-# Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. \
-# Please ensure that your responses are socially unbiased, positive, and focused on enhancing language skills. \
-# If a question does not make sense or is not factually coherent, explain why instead of answering something incorrect. \
 # If you don't know the answer to a question, please don't share false information. \
 # Your role is to guide users through various language exercises and challenges, helping them to practice and improve their English skills in a fun and engaging way. \
 # Always encourage users to try different approaches and provide constructive feedback to help them progress."
-DEFAULT_SYSTEM_PROMPT="\
-You are a helpful, respectful, and honest assistant designed to improve English language skills. Your name is Nemo\
-If you don't know the answer to a question, please don't share false information. \
-Your role is to guide users through various language exercises and challenges, helping them to practice and improve their English skills in a fun and engaging way. \
-Always encourage users to try different approaches and provide constructive feedback to help them progress."
 
-instruction = "Have a good conversation: \n\n {text}"
+# instruction = "Have a good conversation: \n\n {text}"
+
+
+# SYSTEM_PROMPT = B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS
+
+# template = B_INST + SYSTEM_PROMPT + instruction + E_INST
+
+# prompt = PromptTemplate(template=template, input_variables=["text"])
+
+# # llm = CTransformers(model="TheBloke/Llama-2-7B-Chat-GGUF", model_file="llama-2-7b-chat.Q3_K_S.gguf",
+# llm = CTransformers(model="NousResearch/Llama-2-7b-chat-hf",
+#                     model_type='llama',
+#                     config={'max_new_tokens': 128,
+#                             'temperature': 0.01}
+#                     )
+
+# LLM_Chain = LLMChain(prompt=prompt, llm=llm)
+
+# def greet(prompt):
+#     return LLM_Chain.run(prompt)
+
+# iface = gr.Interface(fn=greet, inputs="text", outputs="text")
+# iface.launch()
+
+
+
+
+
+
+
+
+
+
+########################3
+from langchain.chains import RetrievalQA, ConversationalRetrievalChain
+from langchain.vectorstores import Chroma
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.document_loaders import DirectoryLoader, TextLoader,PyPDFLoader
+from transformers import pipeline, AutoModelForCausalLM
+from langchain.llms import HuggingFacePipeline
+from langchain.embeddings import HuggingFaceInstructEmbeddings
+import gradio as gr
+from InstructorEmbedding import INSTRUCTOR
+import torch
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+from transformers import AutoModelForSequenceClassification, AutoTokenizer
+
 
+tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-base")
+model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-base")
 
-SYSTEM_PROMPT = B_SYS + DEFAULT_SYSTEM_PROMPT + E_SYS
+pipe = pipeline(
+    "text2text-generation",
+    model=model,
+    tokenizer=tokenizer,
+    max_length=200,
+    temperature=0.8,
+    top_p=0.95,
+    repetition_penalty=1.15,
+    do_sample=True
+)
 
-template = B_INST + SYSTEM_PROMPT + instruction + E_INST
+local_llm = HuggingFacePipeline(pipeline=pipe)
+loader = PyPDFLoader('conv.pdf')
+# loader = TextLoader('info.txt')
+document = loader.load()
+text_spliter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
+texts = text_spliter.split_documents(document)
+embedding = HuggingFaceInstructEmbeddings()
+docsearch = Chroma.from_documents(texts, embedding, persist_directory='db')
 
-prompt = PromptTemplate(template=template, input_variables=["text"])
+retriever = docsearch.as_retriever(search_kwargs={"k": 3})
+qa_chain = RetrievalQA.from_chain_type(llm=local_llm,
+                                       chain_type="stuff",
+                                       retriever=retriever,
+                                       return_source_documents=True)
 
-# llm = CTransformers(model="TheBloke/Llama-2-7B-Chat-GGUF", model_file="llama-2-7b-chat.Q3_K_S.gguf",
-llm = CTransformers(model="NousResearch/Llama-2-7b-chat-hf",
-                    model_type='llama',
-                    config={'max_new_tokens': 128,
-                            'temperature': 0.01}
-                    )
+def gradinterface(query,history):
+    result = qa_chain({'query': query})
+    return result['result']
 
-LLM_Chain = LLMChain(prompt=prompt, llm=llm)
 
-def greet(prompt):
-    return LLM_Chain.run(prompt)
+demo = gr.ChatInterface(fn=gradinterface, title='OUR_OWN_BOT')
 
-iface = gr.Interface(fn=greet, inputs="text", outputs="text")
-iface.launch()
+if __name__ == "__main__":
+    demo.launch(share=True)