code with harry

chapter-1/file.txt

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+hello welcome to world of programming!
+I am Eva, your personal assistanc. How may I help you today?

chapter-1/problem1.py

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+print('''Twinkle, twinkle, little star,
+How I wonder what you are!
+Up above the world so high,
+Like a diamond in the sky.
+Twinkle, twinkle, little star,
+How I wonder what you are!
+
+When the blazing sun is gone,
+When he nothing shines upon,
+Then you show your little light,
+Twinkle, twinkle, all the night.
+Twinkle, twinkle, little star,
+How I wonder what you are!
+
+Then the traveller in the dark,
+Thanks you for your tiny spark,
+He could not see which way to go,
+If you did not twinkle so.
+Twinkle, twinkle, little star,
+How I wonder what you are!
+
+In the dark blue sky you keep,
+And often through my curtains peep,
+For you never shut your eye,
+Till the sun is in the sky.
+Twinkle, twinkle, little star,
+How I wonder what you are!
+
+As your bright and tiny spark,
+Lights the traveller in the dark,—
+Though I know not what you are,
+Twinkle, twinkle, little star.
+Twinkle, twinkle, little star,
+How I wonder what you are!''')

chapter-1/problem3.py

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+import pyttsx3
+
+engine = pyttsx3.init()
+engine.say("Hello, how are you?")
+engine.runAndWait()

chapter-1/problem4.py

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+import os
+directory_path = '/'
+contents = os.listdir(directory_path)
+print(contents)

chapter-2/01_variables.py

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+a=int(input("Enter a number: "))
+b=int(input("Enter another number: "))
+c=a+b
+print("The sum of", a, "and", b, "is", c,"and type is: ", type(c))

chapter-2/02_datatypes.py

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+xl

done/;project 2-guess the nuber.py

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+import random
+n=random.randint(1,100)
+print("**Guess the number between 1 to 100 **")
+a=-1
+guess=1
+while(a!=n):
+    a=int(input("Enter the number:"))
+    if(a>n):
+        print("Smaller number please!")
+    elif(a<n):
+        print("Greater number please!")
+    guess+=1
+print(f"You won the game in {guess} attempts and the number was {n}")

done/Codec.txt

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+#!/usr/bin/env python
+# coding: utf-8
+
+# Install necessary packages
+!pip install sentence-transformers faiss-cpu langchain numba huggingface-hub
+
+import os
+import json
+import numpy as np
+import faiss
+from sentence_transformers import SentenceTransformer
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from numba import cuda
+from langchain.schema import SystemMessage, HumanMessage, AIMessage
+from langchain.prompts import ChatPromptTemplate
+from langchain_community.llms import LlamaCpp
+from huggingface_hub import hf_hub_download
+
+# Function to extract text and split into chunks
+def extract_and_split_text(pdf_file, chunk_size=300):
+    loader = PyPDFLoader(pdf_file)
+    doc = loader.load_and_split()
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=chunk_size,
+        chunk_overlap=50,
+        length_function=len,
+        add_start_index=True,
+    )
+    chunks = text_splitter.split_documents(doc)
+
+    # Print and check chunks
+    for idx, chunk in enumerate(chunks):
+        print(f"Chunk {idx}:\n{chunk.page_content}\n{'-'*40}")
+
+    # Handle very small and very large chunks
+    chunks = [chunk for chunk in chunks if 50 < len(chunk.page_content) < 2000]
+
+    return chunks
+
+# Function to create embeddings and handle edge cases
+def create_embeddings(chunks, model_path='sentence-transformers/paraphrase-MiniLM-L6-v2'):
+    model = SentenceTransformer(model_path)
+    embeddings = []
+
+    for chunk in chunks:
+        embedding = model.encode(chunk.page_content)
+        if embedding.size == 0:
+            embedding = np.zeros((768,))  # Assuming 768-dimensional embeddings
+        embeddings.append(embedding)
+
+    embeddings = np.vstack(embeddings)
+
+    # Padding and normalization if necessary
+    embeddings = np.pad(embeddings, ((0, 0), (0, 0)), mode='constant', constant_values=0)
+    embeddings = embeddings / np.linalg.norm(embeddings, axis=1, keepdims=True)
+
+    return embeddings
+
+# Function to create FAISS index
+def create_faiss_index(embeddings, index_file):
+    index = faiss.IndexFlatL2(embeddings.shape[1])
+    index.add(embeddings)
+    faiss.write_index(index, index_file)
+    return index
+
+# Function to load FAISS index
+def load_faiss_index(index_file):
+    if not os.path.exists(index_file):
+        raise FileNotFoundError(f"Index file does not exist: {index_file}")
+    return faiss.read_index(index_file)
+
+# Function to perform similarity search
+def similarity_search(index, query_embedding, k=10):
+    query_embedding = np.array(query_embedding).astype('float32')
+    D, I = index.search(np.array([query_embedding]), k)
+    return I[0], D[0]
+
+# Main process
+pdf_file_name = '/content/the-story-of-doctor-dolittle-1-15.pdf'
+DB_file_name = os.path.basename(pdf_file_name).split('.')[0]
+index_file = f"{DB_file_name}.index"
+
+# Extract text and split into chunks
+chunks = extract_and_split_text(pdf_file_name)
+
+# Create embeddings and handle edge cases
+embeddings = create_embeddings(chunks)
+
+# Create FAISS index if it doesn't exist
+if not os.path.exists(index_file):
+    index = create_faiss_index(embeddings, index_file)
+else:
+    index = load_faiss_index(index_file)
+
+# Define and do the embeddings of query
+model = SentenceTransformer('sentence-transformers/paraphrase-MiniLM-L6-v2')
+query = 'who is the author of this book?'
+query_embedding = model.encode(query)
+
+# Perform similarity search
+indices, distances = similarity_search(index, query_embedding, k=10)
+
+# Print top results and check
+print("Top results from similarity search:")
+for idx in indices:
+    print(f"Chunk {idx}:\n{chunks[int(idx)].page_content}\n{'-'*40}")
+
+# Convert searched chunks to JSON format
+searched_chunks = [{"chunk_id": int(idx), "content": chunks[int(idx)].page_content} for idx in indices]
+context_json = json.dumps(searched_chunks, indent=2)
+
+n_gpu_layers = -1
+n_batch = 512
+
+# Use the Hugging Face model directly (replace with an appropriate model if needed)
+model_name = 'TheBloke/Mistral-7B-Instruct-v0.2-GGUF'
+model_file = hf_hub_download(repo_id=model_name, filename='mistral-7b-instruct-v0.2.Q4_K_M.gguf', local_dir='./')
+
+print(f"Downloaded model file path: {model_file}")
+
+llm = LlamaCpp(
+    model_path=model_file,
+    n_gpu_layers=-1,
+    n_batch=512,
+    temperature=.5,
+    n_ctx=10000,
+)
+
+# Define prompt template
+PROMPT_TEMPLATE = """
+Given the following document context in JSON format, answer the question:
+{context}
+Question: {question}
+Answer:
+"""
+
+# Generate sample text for LLM check
+def generate_sample_prompt(question, context_json):
+    prompt_template = ChatPromptTemplate.from_template(PROMPT_TEMPLATE)
+    prompt = prompt_template.format_messages(context=context_json, question=question)
+    return prompt
+
+sample_prompt = generate_sample_prompt('What is the document about?', context_json)
+print("Generated sample prompt:")
+print(sample_prompt)
+
+# Define prompt with context and generate answer
+prompt = generate_sample_prompt(query, context_json)
+
+# Pass query, retrieved context, prompt to LLM to generate answer
+try:
+    response = llm.invoke(prompt)
+    print("Response:")
+    print(response)
+except Exception as e:
+    print(f"Error during model invocation: {e}")

done/Match case.py

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+def https_status(status):
+    match status:
+        case 200:
+            return "ok"
+        case 404:
+            return "unknown"
+        case _:
+            return "default"
+print(https_status(200))
+
+dic1={
+       1:'krishn',
+       2:'Jatav'
+       }
+dic2={
+        2:'Jattu',
+        3:'bhai'
+        }
+merge=dic1 | dic2
+print(merge)

done/calculator by class.py

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+from typing import Union
+
+ID:Union[str,int]=input("Enter your college ID: ")
+x=ID.upper()
+if(x=="20AI016"):
+    print("Welcome Krishn Jatav!")
+else:
+    print("X invalide ID X")

done/celcius_to_fehrnite.py

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+c=int(input("Enter the temprature: "))
+
+def temp(c):
+    feh=c+32
+    return feh
+print("Fehernite: ",temp(c))

done/check to file equale.py

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+with open("name.txt","r") as f1:
+    c1=f1.read()
+with open("copy_name.txt","r") as f2:
+    c2=f2.read()
+if(c1==c2):
+    print("Both file have same content!")
+else:
+    print("Both files are different.")

done/copy file.py

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+with open("name.txt")as f:
+    content=f.read()
+
+with open("copy_name.txt","w") as f:
+    f.write(content)
+

done/copy_name.txt

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+I am AI research Analyst at Biz4Group.
+hello python
+python

done/count the line.py

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+with open("name.txt", "r") as f:
+    lines=f.readlines()
+lineno=1
+for line in lines:
+    if("researcher" in line):
+        print(f"word found at line no:{lineno}")
+        break
+        lineno+=1
+    else:
+        print("not found")

done/demo.py

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+from typing import Union
+ID: Union[str, int] = input("Enter your college ID: ")
+x = ID.upper()
+
+if x == "20AI016":
+    print(" ")
+    print("                         ** Login Successfully **                       ")
+    print(" ")
+    print("                         Welcome Krishn Jatav!                         ")
+    print("""             -- Choose the following Options --
+                                1. Account Info
+                                2. Update Account
+                                3. Game mode
+
+                                                                                """)
+
+    while True:
+        n = int(input("Enter your option: "))
+
+        if n == 1:
+            print("""               -- Account Information --
+                                    Name: Krishn Jatav
+                                    Age : 21
+                                    ID  : 20AI016 
+
+                                    Enter 0 to Exit!""")
+
+        elif n == 2:
+            print(''' ! Service Under Maintenance.
+
+                                    Enter 0 to Exit!''')
+
+        elif n == 3:
+            print("""* Error: No game Found! *
+
+                                    Enter 0 to Exit!""")
+
+        elif n == 0:
+            print("Exiting...")
+            break
+
+        else:
+            print("Invalid option, please try again.")
+
+else:
+    print("** Error: Invalid ID **")

done/demo.txt

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+I am AI research Analyst at Biz4Group.
+hello python
+python

done/demo2.py

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+from typing import Union
+ID:Union[str,int]=input("Enter your ID: ")
+id="20AI016"
+x=ID.upper()
+if x==id:
+    print(" ")
+    print("                         ** Login Successfully **                       ")
+    print(" ")
+    print("                         Welcome Krishn Jatav!                         ")
+    print("""             -- Choose the following Options --
+                                1. Account Info
+                                2. Update Account
+                                3. Game mode
+
+                                                                                """)
+
+    while True:
+        n=int(input("Enter your response: "))
+        if n==1:
+            print("No Account found!")
+
+            print(" Enter 0 for Exit!")
+        elif n==2:
+             print("No Account found!")
+
+             print(" Enter 0 for Exit!")
+        elif n==0:
+            print("Exiting...")
+            break
+else:
+    print(" Invalide ID.")