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from huggingface_hub import InferenceClient
import nltk
import re
import requests
import os
api_key = os.getenv("HF_KEY")
nltk.download('punkt')
nltk.download('punkt_tab')
nltk.download('averaged_perceptron_tagger')
nltk.download('averaged_perceptron_tagger_eng')
client = InferenceClient(api_key=api_key)
def extract_product_info(text):
# Initialize result dictionary
result = {"brand": None, "model": None, "description": None, "price": None}
try:
# Improved regex to prioritize currency-related patterns
price_match = re.search(
r'(\$\s?\d{1,3}(?:,\d{3})*(?:\.\d{2})?|(?:\d{1,3}(?:,\d{3})*(?:\.\d{2})?\s?(?:USD|usd|dollars|DOLLARS)))',
text
)
if price_match:
price = price_match.group().strip()
# Clean up the price format
if "$" in price or "USD" in price or "usd" in price:
result["price"] = re.sub(r'[^\d.]', '', price) # Keep only digits and decimals
else:
result["price"] = price
# Remove the price part from the text to prevent it from being included in the brand/model extraction
text = text.replace(price_match.group(), "").strip()
try:
tokens = nltk.word_tokenize(text)
print(f"Tokens: {tokens}")
except Exception as e:
print(f"Error during tokenization: {e}")
# Fall back to a simple split if tokenization fails
tokens = text.split()
print(f"Fallback tokens: {tokens}")
try:
pos_tags = nltk.pos_tag(tokens)
print(f"POS Tags: {pos_tags}")
except Exception as e:
print(f"Error during POS tagging: {e}")
# If POS tagging fails, create dummy tags
pos_tags = [(word, "NN") for word in tokens]
print(f"Fallback POS Tags: {pos_tags}")
# Extract brand, model, and description
brand_parts = []
model_parts = []
description_parts = []
for word, tag in pos_tags:
if tag == 'NNP' or re.match(r'[A-Za-z0-9-]+', word):
if len(brand_parts) == 0: # Assume the first proper noun is the brand
brand_parts.append(word)
else: # Model number tends to follow the brand
model_parts.append(word)
else:
description_parts.append(word)
# Assign values to the result dictionary
if brand_parts:
result["brand"] = " ".join(brand_parts)
if model_parts:
result["model"] = " ".join(model_parts)
if description_parts:
result["description"] = " ".join(description_parts)
print(f"Extract function returned: {result}")
except Exception as e:
print(f"Unexpected error: {e}")
# Return a fallback result in case of a critical error
result["description"] = text
print(f"Fallback result: {result}")
return result
def extract_info(text):
API_URL = "https://api-inference.huggingface.co/models/google/flan-t5-large"
headers = {"Authorization": f"Bearer {api_key}"}
payload = {"inputs": f"From the given text, extract brand name, model number, description about it, and its average price in today's market. Give me back a python dictionary with keys as brand_name, model_number, desc, price. The text is {text}.",}
response = requests.post(API_URL, headers=headers, json=payload)
print('GOOGLEE LLM OUTPUTTTTTTT\n\n',response )
output = response.json()
print(output)
def get_name(url, object):
messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": f"Is this a {object}?. Can you guess what it is and give me the closest brand it resembles to? or a model number? And give me its average price in today's market in USD. In output, give me its normal name, model name, model number and price. separated by commas. No description is needed."
},
{
"type": "image_url",
"image_url": {
"url": url
}
}
]
}
]
completion = client.chat.completions.create(
model="meta-llama/Llama-3.2-11B-Vision-Instruct",
messages=messages,
max_tokens=500
)
print(f'\n\nNow output of LLM:\n')
llm_result = completion.choices[0].message['content']
print(llm_result)
# print(f'\n\nThat is the output')
print(f"Extracting from the output now, function calling")
result = extract_product_info(llm_result)
print(f'\n\nResult brand and price:{result}')
print(f'\n\nThat is the output')
# result2 = extract_info(llm_result)
# print(f'\n\nFrom Google llm:{result2}')
return result
# url = "https://i.ibb.co/mNYvqDL/crop_39.jpg"
# object="fridge"
# get_name(url, object) |