Add application file

Dockerfile

@@ -0,0 +1,10 @@
+FROM python:3.12-alpine
+
+COPY . .
+
+WORKDIR /
+
+RUN pip install --no-cache-dir --upgrade -r /requirements.txt
+
+CMD ["python3","main.py"]
+

llm_inference/.env

@@ -0,0 +1 @@
+GROQ_API_KEY=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

llm_inference/.env.example

@@ -0,0 +1 @@
+GROQ_API_KEY=

llm_inference/poetry.lock

@@ -0,0 +1,325 @@
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+
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+optional = false
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+optional = false
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+
+[[package]]
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+version = "2024.8.30"
+description = "Python package for providing Mozilla's CA Bundle."
+optional = false
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+]
+
+[package.dependencies]
+typing-extensions = ">=4.6.0,<4.7.0 || >4.7.0"
+
+[[package]]
+name = "python-dotenv"
+version = "1.0.1"
+description = "Read key-value pairs from a .env file and set them as environment variables"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "python-dotenv-1.0.1.tar.gz", hash = "sha256:e324ee90a023d808f1959c46bcbc04446a10ced277783dc6ee09987c37ec10ca"},
+    {file = "python_dotenv-1.0.1-py3-none-any.whl", hash = "sha256:f7b63ef50f1b690dddf550d03497b66d609393b40b564ed0d674909a68ebf16a"},
+]
+
+[package.extras]
+cli = ["click (>=5.0)"]
+
+[[package]]
+name = "sniffio"
+version = "1.3.1"
+description = "Sniff out which async library your code is running under"
+optional = false
+python-versions = ">=3.7"
+files = [
+    {file = "sniffio-1.3.1-py3-none-any.whl", hash = "sha256:2f6da418d1f1e0fddd844478f41680e794e6051915791a034ff65e5f100525a2"},
+    {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
+]
+
+[[package]]
+name = "typing-extensions"
+version = "4.12.2"
+description = "Backported and Experimental Type Hints for Python 3.8+"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
+    {file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
+]
+
+[metadata]
+lock-version = "2.0"
+python-versions = "^3.10"
+content-hash = "3870166940d342433359f9ca17ab03c970e49921d2fdd8964ec735f3b4411ab7"

llm_inference/pyproject.toml

@@ -0,0 +1,17 @@
+[tool.poetry]
+name = "hackathon-mlh"
+version = "0.1.0"
+description = ""
+authors = ["Prashjeev Rai <prashjeevrai@gmail.com>"]
+readme = "README.md"
+packages = [{include = "hackathon_mlh", from = "src"}]
+
+[tool.poetry.dependencies]
+python = "^3.10"
+groq = "^0.11.0"
+python-dotenv = "^1.0.1"
+
+
+[build-system]
+requires = ["poetry-core"]
+build-backend = "poetry.core.masonry.api"

llm_inference/src/llm_inference/__init__.py

@@ -0,0 +1,3 @@
+from .inference import html_parser, required_content_bool
+
+__all__ = ["html_parser", "required_content_bool"]

llm_inference/src/llm_inference/configs/configs.json

@@ -0,0 +1,4 @@
+{
+    "model_name": "llama3-70b-8192",
+    "temperature": 0
+}

llm_inference/src/llm_inference/configs/logging.conf

@@ -0,0 +1,28 @@
+[loggers]
+keys=root,mainLogger
+
+[handlers]
+keys=fileHandler
+
+[formatters]
+keys=simpleFormatter
+
+[logger_root]
+level=INFO
+handlers=fileHandler
+
+[logger_mainLogger]
+level=INFO
+handlers=fileHandler
+qualname=__main__
+propagate=0
+
+[handler_fileHandler]
+class=FileHandler
+level=INFO
+formatter=simpleFormatter
+args=(r"ai-backend\llm_inference\src\llm_inference\logging\program.log",'w')
+
+[formatter_simpleFormatter]
+format=%(asctime)s - %(name)s - %(levelname)s - %(message)s
+datefmt=%Y-%m-%d %H:%M:%S

llm_inference/src/llm_inference/helper_functions/__init__.py

@@ -0,0 +1,16 @@
+from .load_and_extract import (
+    load_prompt,
+    save_responses_to_file,
+    load_config,
+    load_html_file
+)
+
+from .call_llm import PromptProcessor
+
+__all__ = [
+    "load_prompt",
+    "save_responses_to_file",
+    "load_config",
+    "PromptProcessor",
+    "load_html_file"
+]

llm_inference/src/llm_inference/helper_functions/call_llm.py

@@ -0,0 +1,96 @@
+from dotenv import load_dotenv
+import asyncio
+import logging
+from groq import Groq
+
+from .load_and_extract import load_config, load_prompt
+
+
+class PromptProcessor:
+    def __init__(self, config_path: str):
+        # Load environment variables
+        load_dotenv()
+        # Load configuration
+        self.config = load_config(config_path)
+        # Initialize the LLM
+        self.client = Groq()
+
+    async def _async_prompting(
+        self,
+        content: str,
+        prompt_file_path: str,
+        logger: logging.Logger,
+    ):
+        logger = logging.getLogger(__name__)
+        try:
+            try:
+                # Load the prompt
+                prompt = load_prompt(file_path=prompt_file_path)
+            except Exception as e:
+                logger.fatal(f"Failed to load prompt: {e}")
+                raise
+
+            chat_completion = self.client.chat.completions.create(
+                model=self.config.get("model_name"),
+                temperature=self.config.get("temperature"),
+                messages=[
+                    {
+                        "role": "system",
+                        "content": prompt,
+                    },
+                    {
+                        "role": "user",
+                        "content": content
+                    }
+
+                ]
+            )
+
+            response = chat_completion.choices[0].message.content
+            logger.info("Result: Output received")
+            return response
+        except Exception as e:
+            logger.error(f"Error in _async_prompting: {repr(e)}")
+            return e
+
+    async def prompting_with_timeout(
+        self,
+        prompt_path: str,
+        data_string: str,
+        logger: logging.Logger,
+        timeout: int = 500,
+    ):
+        logger = logging.getLogger(__name__)
+        try:
+            # Call the async prompt function with a timeout
+            output = await asyncio.wait_for(
+                self._async_prompting(
+                    content=data_string,
+                    prompt_file_path=prompt_path,
+                    logger=logger
+                ),
+                timeout,
+            )
+            return output
+        except asyncio.TimeoutError:
+            logger.warning("Result: Operation timed out")
+        except Exception as e:
+            logger.warning(f"Unexpected error: {e}")
+
+
+# Usage Example
+if __name__ == "__main__":
+    config_path = "src/post_ocr_extraction/configs/config.json"
+    processor = PromptProcessor(config_path)
+    prompt_path = "path/to/prompt/file"
+    data_string = "Your data string here"
+    timeout = 10  # seconds
+
+    # Run the asynchronous method
+    async def run():
+        result = await processor.prompting_with_timeout(
+            prompt_path, data_string, timeout
+        )
+        print(result)
+
+    asyncio.run(run())

llm_inference/src/llm_inference/helper_functions/load_and_extract.py

@@ -0,0 +1,66 @@
+import json
+import os
+
+
+def load_prompt(file_path: str) -> str:
+    with open(file_path, "r", encoding="utf-8") as file:
+        return file.read()
+
+
+def save_responses_to_file(
+    responses: str,
+    filename: str,
+    directory: str,
+):
+    if not os.path.exists(directory):
+        os.makedirs(directory)
+
+    filepath = os.path.join(directory, filename)
+
+    with open(filepath, "w") as file:
+        for i, response in enumerate(responses):
+            file.write(f"Response Number {i}:\n")
+
+            # Convert response to JSON string
+            if isinstance(response, dict):
+                response_str = json.dumps(
+                    response, indent=4
+                )  # Format the dict as a JSON string
+            else:
+                response_str = str(response)  # Handle other types (e.g., strings)
+
+            file.write(response_str + "\n\n")
+
+
+def load_config(config_path: str):
+    """Load configuration from a JSON file."""
+    try:
+        with open(config_path, "r") as file:
+            config = json.load(file)
+        return config
+    except FileNotFoundError:
+        raise FileNotFoundError(f"Configuration file not found: {config_path}")
+    except json.JSONDecodeError:
+        raise ValueError(f"Error decoding JSON from the file: {config_path}")
+    
+
+def load_html_file(file_path:str):
+    """
+    Loads the content of an HTML file from the given file path.
+
+    Args:
+        file_path (str): The path to the HTML file.
+
+    Returns:
+        str: The content of the HTML file as a string.
+    """
+    try:
+        with open(file_path, 'r', encoding='utf-8') as file:
+            html_content = file.read()
+        return html_content
+    except FileNotFoundError:
+        print(f"Error: The file at path '{file_path}' was not found.")
+        return None
+    except Exception as e:
+        print(f"Error: An unexpected error occurred - {e}")
+        return None

llm_inference/src/llm_inference/inference.py

@@ -0,0 +1,69 @@
+import logging.config
+import asyncio
+from .helper_functions import PromptProcessor, load_html_file
+import os
+
+# Configuration file paths
+config_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "configs", "configs.json"))
+# html_file_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "html_for_testing", "test.html"))
+html_parser_prompt_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "prompts", "extract_from_html.txt"))
+comparision_prompt_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "prompts", "comparision.txt"))
+
+# Load HTML content
+# html_file = load_html_file(file_path=html_file_path)
+
+# Configure logging
+logging_config_path = os.path.abspath(os.path.join(os.path.dirname(__file__), "configs", "logging.conf"))
+logging.config.fileConfig(logging_config_path)
+
+# Get logger
+logger = logging.getLogger(__name__)
+
+# Initialize the LLM processor
+llm = PromptProcessor(config_path=config_path)
+
+# Define an asynchronous function to run the prompt
+async def html_parser(content: str) -> str:
+    """
+    Parameters:
+        content: This is the html content from which the LLM extracts relevant
+        information.
+    Output:
+        The relevant information in str format.
+    """
+    # Await the async prompt method
+    return await llm._async_prompting(content=content, prompt_file_path=html_parser_prompt_path, logger=logger)
+
+async def required_content_bool(content: str, user_req: str) -> bool:
+    """
+    Parameters:
+        content: The relevant information extracted from the webpage.
+        user_req: The product/information the user requested
+    Output:
+        True or False indicating whether the webpage contains the product/information requested by the user
+    """
+    # Format the input for the LLM
+    prompt = f"""
+    Website Content: {content}
+    User Query: {user_req}
+    
+    Compare the website content with the user's query and determine whether the website contains the requested information.
+    Your response should be either "yes" or "no".
+    """
+    comparision_output = await llm._async_prompting(content=prompt, prompt_file_path=comparision_prompt_path, logger=logger)
+
+    # Check the LLM response and map to boolean
+    if comparision_output.strip().lower() == "yes":
+        logger.info("Returned positive boolean")
+        return True
+    elif comparision_output.strip().lower() == "no":
+        logger.info("Returned negative boolean")
+        return False
+    else:
+        logger.fatal("Did not return a boolean, raising value error")
+        raise ValueError("Unexpected response from LLM. Expected 'yes' or 'no'.")
+
+
+# Run the asynchronous function
+# if __name__ == "__main__":
+#     print(asyncio.run(required_content_bool(content="This website contains information aboout dogs", user_req="I want cats")))

llm_inference/src/llm_inference/prompts/comparision.txt

@@ -0,0 +1,18 @@
+You will receive two pieces of information:
+
+Website Content: A long block of text containing the content of a webpage. This
+includes all the information available on the page (e.g., titles, paragraphs,
+lists, links, etc.).
+
+User Query: A short string describing what the user is looking for. It may be a
+specific topic, product, or piece of information.
+
+Your task is to:
+
+Compare the website content with the user's query.
+Determine whether the website content contains the information that the user is
+asking for.
+Your response should be a single word: either "yes" or "no".
+
+"Yes": If the website contains the information the user is asking for.
+"No": If the website does not contain the requested information.

llm_inference/src/llm_inference/prompts/extract_from_html.txt

@@ -0,0 +1,55 @@
+You are tasked with extracting useful and relevant information from the given
+HTML file. Your goal is to analyze the content of the HTML page and provide a
+structured summary of the key data, focusing only on the textual content and
+excluding any HTML code, scripts, or style information. The following steps
+outline the information you should extract:
+
+Page Title and Meta Information:
+
+Extract the title of the page (if available) and provide a brief description.
+Extract any meta description and keywords, if provided, and summarize their
+content.
+Identify and summarize any other useful meta information, such as author,
+language, or character set.
+Headings and Structure:
+
+List the main headings on the page (e.g., titles, sections) in the order they
+appear.
+Provide a summary of the page's structure, highlighting the major sections
+and sub-sections based on these headings.
+Text Content:
+Extract and summarize all significant visible text on the page, excluding
+navigation, footer, or boilerplate content.
+Focus on paragraphs, lists, and other important written content that convey
+the main message of the page.
+Links and URLs:
+
+Identify and summarize the key hyperlinks (URLs) on the page, including
+important internal and external links.
+Provide a brief description of what each link points to, where relevant.
+Images:
+
+Identify and summarize the images on the page, providing descriptions of their
+content based on any available alt text or surrounding context (avoid listing
+technical attributes such as "src" or "alt").
+Summarize the role or context of the images as they appear in the content.
+Tables and Lists:
+
+If there are any tables or lists on the page, summarize their content in a
+readable format, focusing on the main data or information being presented.
+Other Relevant Information:
+
+Identify and summarize any other key elements of the page, such as embedded
+videos, forms, or other types of media or content that contribute to the main
+purpose of the page.
+Avoid including any code, scripts, or style details.
+Important Notes:
+Natural Language Focus: Your goal is to provide a structured summary in natural
+language, focusing on the content that a human reader would find useful and
+relevant.
+Omit Technical Details: Do not include any technical details about the HTML
+structure, such as tags, classes, or scripts. Focus purely on the meaningful
+content and information presented to the user.
+Ensure that the extracted information is clear, concise, and presented in a
+logical order, helping the reader easily understand the key points of the
+webpage's content.

main.py

@@ -0,0 +1,28 @@
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi import HTTPException
+from llm_inference.src.llm_inference.inference import html_parser, required_content_bool
+
+app = FastAPI()
+
+# Configure CORS
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],  # In production, replace with specific origins
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+@app.post("/run_prompt")
+async def api_run_prompt(content: str, user_req: str):
+    try:
+        parsed_response = await html_parser(content)
+        required_content = await required_content_bool(parsed_response, user_req)
+        return {"response": required_content}
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=str(e))
+
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, port=7860)

requirements.txt

@@ -0,0 +1,86 @@
+attrs==23.2.0
+autocommand==2.2.2
+Babel==2.14.0
+bcc==0.30.0
+bcrypt==4.2.0
+blinker==1.8.2
+Brlapi==0.8.5
+certifi==2024.6.2
+chardet==5.2.0
+click==8.1.7
+cloud-init==24.3.1
+colorama==0.4.6
+command-not-found==0.3
+configobj==5.0.8
+cryptography==42.0.5
+cupshelpers==1.0
+dbus-python==1.3.2
+defer==1.0.6
+distro==1.9.0
+distro-info==1.9
+duplicity==2.1.4
+fasteners==0.18
+gyp-next==0.16.2
+httplib2==0.22.0
+idna==3.6
+inflect==7.3.1
+jaraco.context==6.0.0
+jaraco.functools==4.0.2
+Jinja2==3.1.3
+jsonpatch==1.32
+jsonpointer==2.0
+jsonschema==4.19.2
+jsonschema-specifications==2023.12.1
+language-selector==0.1
+launchpadlib==2.0.0
+lazr.restfulclient==0.14.6
+lazr.uri==1.0.6
+louis==3.30.0
+Mako==1.3.5.dev0
+markdown-it-py==3.0.0
+MarkupSafe==2.1.5
+mdurl==0.1.2
+monotonic==1.6
+more-itertools==10.3.0
+netaddr==0.10.1
+netifaces==0.11.0
+oauthlib==3.2.2
+olefile==0.46
+packaging==24.1
+paramiko==3.4.0
+pexpect==4.9.0
+pillow==10.4.0
+ptyprocess==0.7.0
+pycairo==1.26.1
+pycups==2.0.4
+Pygments==2.18.0
+PyGObject==3.48.2
+PyJWT==2.7.0
+PyNaCl==1.5.0
+pyparsing==3.1.2
+pyserial==3.5
+python-apt==2.9.0+ubuntu1
+python-dateutil==2.9.0
+python-debian==0.1.49+ubuntu3
+pytz==2024.1
+pyxdg==0.28
+PyYAML==6.0.2
+referencing==0.35.1
+requests==2.32.3
+rich==13.7.1
+rpds-py==0.20.0
+screen-resolution-extra==0.0.0
+six==1.16.0
+systemd-python==235
+typeguard==4.3.0
+typing_extensions==4.12.2
+ubuntu-drivers-common==0.0.0
+ubuntu-pro-client==8001
+ufw==0.36.2
+unattended-upgrades==0.1
+urllib3==2.0.7
+usb-creator==0.3.16
+wadllib==1.3.6
+wheel==0.44.0
+xdg==5
+xkit==0.0.0