updated nltk

llm/inference.py

@@ -1,119 +1,121 @@
-from huggingface_hub import InferenceClient
-import nltk
-import re 
-import requests
-import os 
-
-api_key = os.getenv("HF_KEY")
-
-nltk.download('punkt_tab')
-nltk.download('averaged_perceptron_tagger')
-
-
-client = InferenceClient(api_key=api_key)
-
-
-def extract_product_info(text):
-    # Initialize result dictionary
-    result = {"brand": None, "model": None, "description": None, "price": None}
-
-    # 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()
-
-    # Tokenize the remaining text and tag parts of speech
-    tokens = nltk.word_tokenize(text)
-    pos_tags = nltk.pos_tag(tokens)
-
-    # Extract brand and model (Proper Nouns + Alphanumeric patterns)
-    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 brand and model to result dictionary
-    if brand_parts:
-        result["brand"] = " ".join(brand_parts)
-    if model_parts:
-        result["model"] = " ".join(model_parts)
-    
-    # Combine the remaining parts as description
-    result["description"] = " ".join(description_parts)
-
-    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')
-     
-	result = extract_product_info(llm_result)
-	print(f'\n\nResult brand and price:{result}')
-    
-	# 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"
-
+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}
+
+    # 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()
+
+    # Tokenize the remaining text and tag parts of speech
+    tokens = nltk.word_tokenize(text)
+    pos_tags = nltk.pos_tag(tokens)
+
+    # Extract brand and model (Proper Nouns + Alphanumeric patterns)
+    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 brand and model to result dictionary
+    if brand_parts:
+        result["brand"] = " ".join(brand_parts)
+    if model_parts:
+        result["model"] = " ".join(model_parts)
+    
+    # Combine the remaining parts as description
+    result["description"] = " ".join(description_parts)
+
+    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')
+     
+	result = extract_product_info(llm_result)
+	print(f'\n\nResult brand and price:{result}')
+    
+	# 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)