تجربة انشاء نموذج ذكاء اصطناعي بسيط بلغة بايثون | الجزء الثالث

تجربة انشاء نموذج ذكاء اصطناعي بسيط بلغة بايثون | الجزء الثالث

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my spotify: open.spotify.com/show/6SmOwZlltLrYBhdMa18AJ6

code :

python
import requests
import json
import nltk
from nltk.tokenize import word_tokenize, sent_tokenize
from nltk.corpus import stopwords
from nltk.stem import WordNetLemmatizer

# Set up your Google Custom Search API credentials
api_key = “YOUR_API_KEY”
search_engine_id = “YOUR_SEARCH_ENGINE_ID”# Function to preprocess textdef preprocess_text(text):
# Tokenize the text
tokens = word_tokenize(text)

# Remove stopwords
stop_words = set(stopwords.words(‘english’))
filtered_tokens = [token for token in tokens if token.lower() not in stop_words]

# Lemmatize the tokens
lemmatizer = WordNetLemmatizer()
lemmatized_tokens = [lemmatizer.lemmatize(token) for token in filtered_tokens]

return lemmatized_tokens

# Function to perform a search and retrieve code snippetsdef search_code(query):
url = f”https://www.googleapis.com/customsearch/v1?key={api_key}&cx={search_engine_id}&q={query}”
response = requests.get(url)
data = json.loads(response.text)

code_snippets = [] if ‘items’ in data:
for item in data[‘items’]:
if ‘snippet’ in item:
code_snippets.append(item[‘snippet’])

return code_snippets

# Function to answer a programming questiondef answer_question(question):
# Preprocess the question
question_tokens = preprocess_text(question)

# Search for code snippets
query = f”{question} code snippet”
code_snippets = search_code(query)

# Find the most relevant code snippet based on the question
max_score = 0
best_snippet = Nonefor snippet in code_snippets:
snippet_tokens = preprocess_text(snippet)
score = nltk.jaccard_distance(set(question_tokens), set(snippet_tokens))
if score > max_score:
max_score = score
best_snippet = snippet

return best_snippet

# Example usage: Answer a programming question from the internet
question = “How to sort a list in Python?”
answer = answer_question(question)