Delete app_30_5_24.py

app_30_5_24.py

@@ -1,218 +0,0 @@
-import torch
-from torch import cuda, bfloat16
-from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline, BitsAndBytesConfig, StoppingCriteria, StoppingCriteriaList
-from langchain.llms import HuggingFacePipeline
-from langchain.vectorstores import FAISS
-from langchain.chains import ConversationalRetrievalChain
-import gradio as gr
-from langchain.embeddings import HuggingFaceEmbeddings
-import os
-
-# Load the Hugging Face token from environment
-HF_TOKEN = os.environ.get("HF_TOKEN", None)
-
-# Define stopping criteria
-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
-
-# Load the LLaMA model and tokenizer
-# model_id = 'meta-llama/Meta-Llama-3-8B-Instruct'
-# model_id= "meta-llama/Llama-2-7b-chat-hf"
-model_id="mistralai/Mistral-7B-Instruct-v0.2"
-device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'
-
-# Set quantization configuration
-bnb_config = BitsAndBytesConfig(
-    load_in_4bit=True,
-    bnb_4bit_quant_type='nf4',
-    bnb_4bit_use_double_quant=True,
-    bnb_4bit_compute_dtype=bfloat16
-)
-
-tokenizer = AutoTokenizer.from_pretrained(model_id, token=HF_TOKEN)
-model = AutoModelForCausalLM.from_pretrained(model_id, device_map="auto", token=HF_TOKEN, quantization_config=bnb_config)
-
-# Define stopping criteria
-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]
-stopping_criteria = StoppingCriteriaList([StopOnTokens()])
-
-# Create text generation pipeline
-generate_text = pipeline(
-    model=model,
-    tokenizer=tokenizer,
-    return_full_text=True,
-    task='text-generation',
-    # stopping_criteria=stopping_criteria,
-    temperature=0.1,
-    max_new_tokens=2048,
-    # repetition_penalty=1.1
-)
-
-llm = HuggingFacePipeline(pipeline=generate_text)
-
-# Load the stored FAISS index
-try:
-    vectorstore = FAISS.load_local('faiss_index', HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2", model_kwargs={"device": "cuda"}))
-    print("Loaded embedding successfully")
-except ImportError as e:
-    print("FAISS could not be imported. Make sure FAISS is installed correctly.")
-    raise e
-
-# Set up the Conversational Retrieval Chain
-chain = ConversationalRetrievalChain.from_llm(llm, vectorstore.as_retriever(), return_source_documents=True)
-
-chat_history = []
-
-def format_prompt(query):
-    prompt=f"""
-    You are a knowledgeable assistant with access to a comprehensive database. 
-    I need you to answer my question and provide related information in a specific format.
-    I have provided four relatable json files , choose the most suitable chunks for answering the query
-    Here's what I need:
-    Include a final answer without additional comments, sign-offs, or extra phrases. Be direct and to the point.
-    
-    Here's my question:
-    {query}
-
-    Solution==>
-
-    Example1
-    Query: "How to use IPU1_0 instead of A15_0 to process NDK in TDA2x-EVM",
-    Solution: "To use IPU1_0 instead of A15_0 to process NDK in TDA2x-EVM, you need to modify the configuration file of the NDK application. Specifically, change the processor reference from 'A15_0' to 'IPU1_0'.",
-    
-    Example2
-    Query: "Can BQ25896 support I2C interface?",
-    Solution: "Yes, the BQ25896 charger supports the I2C interface for communication.",
-    """
-    # The format I want answer in 
-    # user_query ==> query
-    # response   ==>  
-    # """
-    # prompt = f"""
-    # You are a knowledgeable assistant with access to a comprehensive database. 
-    # I need you to answer my question and provide related information in a specific format.
-    # Here's what I need:
-    # A brief, general response to my question based on related answers retrieved.
-    # Include a brief final answer without additional comments, sign-offs, or extra phrases. Be direct and to the point.
-    
-    # A JSON-formatted output containing: ALL SOURCE DOCUMENTS
-    #    - "question": The ticketName
-    #    - "answer": The Responses
-    # Here's my question:
-    # {query}
-    # """
-       
-    #    - "related_questions": A list of related questions and their answers, each as a dictionary with the keys. Consider all source documents:
-    #      - "question": The related question.
-    #      - "answer": The related answer.
-    
-    
-
-    # Example 1:
-    # {{
-    #     "question": "How to use IPU1_0 instead of A15_0 to process NDK in TDA2x-EVM",
-    #     "answer": "To use IPU1_0 instead of A15_0 to process NDK in TDA2x-EVM, you need to modify the configuration file of the NDK application. Specifically, change the processor reference from 'A15_0' to 'IPU1_0'.",
-    #     "related_questions": [
-    #         {{
-    #             "question": "Can you provide MLBP documentation on TDA2?",
-    #             "answer": "MLB is documented for DRA devices in the TRM book, chapter 24.12."
-    #         }},
-    #         {{
-    #             "question": "Hi, could you share me the TDA2x documents about Security(SPRUHS7) and Cryptographic(SPRUHS8) addendums?",
-    #             "answer": "Most of TDA2 documents are on ti.com under the product folder."
-    #         }},
-    #         {{
-    #             "question": "Is any one can provide us a way to access CDDS for nessary docs?",
-    #             "answer": "Which document are you looking for?"
-    #         }},
-    #         {{
-    #             "question": "What can you tell me about the TDA2 and TDA3 processors? Can they / do they run Linux?",
-    #             "answer": "We have moved your post to the appropriate forum."
-    #         }}
-    #     ]
-    # }}
-
-    # Final Answer: To use IPU1_0 instead of A15_0 to process NDK in TDA2x-EVM, you need to modify the configuration file of the NDK application. Specifically, change the processor reference from 'A15_0' to 'IPU1_0'.
-    
-    # Example 2:
-    # {{
-    #     "question": "Can BQ25896 support I2C interface?",
-    #     "answer": "Yes, the BQ25896 charger supports the I2C interface for communication.",
-    #     "related_questions": [
-    #         {{
-    #             "question": "What are the main features of BQ25896?",
-    #             "answer": "The BQ25896 features include high-efficiency, fast charging capability, and a wide input voltage range."
-    #         }},
-    #         {{
-    #             "question": "How to configure the BQ25896 for USB charging?",
-    #             "answer": "To configure the BQ25896 for USB charging, set the input current limit and the charging current via I2C registers."
-    #         }}
-    #     ]
-    # }}
-
-    # Final Answer: Yes, the BQ25896 charger supports the I2C interface for communication.
-    
-    # """
-
-    
-    return prompt
-
-
-def qa_infer(query):
-    content = ""
-    formatted_prompt = format_prompt(query)
-    result = chain({"question": formatted_prompt, "chat_history": chat_history})
-    for doc in result['source_documents']:
-        content += "-" * 50 + "\n"
-        content += doc.page_content + "\n"
-    print(content)
-    print("#" * 100)
-    print(result['answer'])
-    # return content , result['answer']
-
-
-    # Save the output to a file
-    output_file = "output.txt"
-    with open(output_file, "w") as f:
-        f.write("Query:\n")
-        f.write(query + "\n\n")
-        f.write("Answer:\n")
-        f.write(result['answer'] + "\n\n")
-        f.write("Source Documents:\n")
-        f.write(content + "\n")
-
-    # Return the content and answer along with the download link
-    download_link = f'<a href="file/{output_file}" download>Download Output File</a>'
-    return result['answer'],content, download_link
-    
-css_code = """
-        .gradio-container {
-            background-color: #daccdb;
-        }
-    
-        /* Button styling for all buttons */
-        button {
-            background-color: #927fc7; /* Default color for all other buttons */
-            color: black;
-            border: 1px solid black;
-            padding: 10px;
-            margin-right: 10px;
-            font-size: 16px; /* Increase font size */
-            font-weight: bold; /* Make text bold */
-        }
-    
-
-        """
-EXAMPLES = ["TDA4 product planning and datasheet release progress? ", 
-            "I'm using Code Composer Studio 5.4.0.00091 and enabled FPv4SPD16 floating point support for CortexM4 in TDA2. However, after building the project, the .asm file shows --float_support=vfplib instead of FPv4SPD16. Why is this happening?", 
-            "Master core in TDA2XX is a15 and in TDA3XX it is m4,so we have to shift all modules that are being used by a15 in TDA2XX to m4 in TDA3xx."]
-
-demo = gr.Interface(fn=qa_infer, inputs=[gr.Textbox(label="QUERY", placeholder ="Enter your query here")], allow_flagging='never', examples=EXAMPLES, cache_examples=False, outputs=[gr.Textbox(label="SOLUTION"), gr.Textbox(label="RELATED QUERIES"), gr.HTML()], css=css_code)#,outputs="text")
-demo.launch()
-