Create app.py
Browse files
app.py
ADDED
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1 |
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import gradio as gr
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import os
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import nest_asyncio
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4 |
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import re
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from pathlib import Path
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import typing as t
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import base64
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from mimetypes import guess_type
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from llama_parse import LlamaParse
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from llama_index.core.schema import TextNode
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from llama_index.core import VectorStoreIndex, StorageContext, load_index_from_storage, Settings
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from llama_index.embeddings.openai import OpenAIEmbedding
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from llama_index.llms.openai import OpenAI
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from llama_index.core.query_engine import CustomQueryEngine
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from llama_index.multi_modal_llms.openai import OpenAIMultiModal
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from llama_index.core.prompts import PromptTemplate
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from llama_index.core.schema import ImageNode
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from llama_index.core.base.response.schema import Response
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from typing import Any, List, Optional
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from llama_index.core.postprocessor.types import BaseNodePostprocessor
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nest_asyncio.apply()
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# Setting API keys
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os.environ["OPENAI_API_KEY"] = os.getenv('OPENAI_API_KEY')
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+
os.environ["LLAMA_CLOUD_API_KEY"] = os.getenv('LLAMA_CLOUD_API_KEY')
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+
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# Initialize the parser
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parser = LlamaParse(
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result_type="markdown",
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parsing_instruction="You are given a medical textbook on medicine",
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use_vendor_multimodal_model=True,
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vendor_multimodal_model_name="gpt-4o-mini-2024-07-18",
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show_progress=True,
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verbose=True,
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invalidate_cache=True,
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do_not_cache=True,
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num_workers=8,
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language="en"
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)
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# Function to encode image to data URL
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def local_image_to_data_url(image_path):
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44 |
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mime_type, _ = guess_type(image_path)
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45 |
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if mime_type is None:
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46 |
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mime_type = 'image/png'
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47 |
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with open(image_path, "rb") as image_file:
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base64_encoded_data = base64.b64encode(image_file.read()).decode('utf-8')
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return f"data:{mime_type};base64,{base64_encoded_data}"
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+
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# Function to get sorted image files
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def get_page_number(file_name):
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53 |
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match = re.search(r"-page-(\d+)\.jpg$", str(file_name))
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54 |
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if match:
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55 |
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return int(match.group(1))
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return 0
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def _get_sorted_image_files(image_dir):
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raw_files = [f for f in list(Path(image_dir).iterdir()) if f.is_file()]
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sorted_files = sorted(raw_files, key=get_page_number)
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61 |
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return sorted_files
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63 |
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def get_text_nodes(md_json_objs, image_dir) -> t.List[TextNode]:
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64 |
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nodes = []
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for result in md_json_objs:
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json_dicts = result["pages"]
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document_name = result["file_path"].split('/')[-1]
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docs = [doc["md"] for doc in json_dicts]
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69 |
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image_files = _get_sorted_image_files(image_dir)
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70 |
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for idx, doc in enumerate(docs):
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node = TextNode(
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text=doc,
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metadata={"image_path": str(image_files[idx]), "page_num": idx + 1, "document_name": document_name},
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)
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nodes.append(node)
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return nodes
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78 |
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# Gradio interface functions
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79 |
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def upload_and_process_file(uploaded_file):
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80 |
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if uploaded_file is None:
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81 |
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return "Please upload a medical textbook (pdf)"
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82 |
+
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83 |
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file_path = f"{uploaded_file.name}"
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with open(file_path, "wb") as f:
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f.write(uploaded_file.read())
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87 |
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md_json_objs = parser.get_json_result([file_path])
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image_dicts = parser.get_images(md_json_objs, download_path="data_images")
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89 |
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90 |
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return md_json_objs
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92 |
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def ask_question(md_json_objs, query_text, uploaded_query_image=None):
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if not md_json_objs:
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return "No knowledge base loaded. Please upload a file first."
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text_nodes = get_text_nodes(md_json_objs, "data_images")
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# Setup index and LLM
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embed_model = OpenAIEmbedding(model="text-embedding-3-large")
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llm = OpenAI("gpt-4o-mini-2024-07-18")
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Settings.llm = llm
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Settings.embed_model = embed_model
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if not os.path.exists("storage_manuals"):
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index = VectorStoreIndex(text_nodes, embed_model=embed_model)
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index.storage_context.persist(persist_dir="./storage_manuals")
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else:
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ctx = StorageContext.from_defaults(persist_dir="./storage_manuals")
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index = load_index_from_storage(ctx)
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retriever = index.as_retriever()
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# Encode query image if provided
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encoded_image_url = None
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if uploaded_query_image is not None:
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query_image_path = f"{uploaded_query_image.name}"
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with open(query_image_path, "wb") as img_file:
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img_file.write(uploaded_query_image.read())
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encoded_image_url = local_image_to_data_url(query_image_path)
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# Setup query engine
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QA_PROMPT_TMPL = """
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+
You are a friendly medical chatbot designed to assist users by providing accurate and detailed responses to medical questions based on information from medical books.
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+
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### Context:
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---------------------
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{context_str}
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---------------------
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129 |
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### Query Text:
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{query_str}
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133 |
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### Query Image:
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---------------------
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{encoded_image_url}
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---------------------
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### Answer:
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"""
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QA_PROMPT = PromptTemplate(QA_PROMPT_TMPL)
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gpt_4o_mm = OpenAIMultiModal(model="gpt-4o-mini-2024-07-18")
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142 |
+
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143 |
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class MultimodalQueryEngine(CustomQueryEngine):
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qa_prompt: PromptTemplate
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+
retriever: BaseRetriever
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146 |
+
multi_modal_llm: OpenAIMultiModal
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147 |
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node_postprocessors: Optional[List[BaseNodePostprocessor]]
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148 |
+
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149 |
+
def __init__(
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self,
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qa_prompt: PromptTemplate,
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152 |
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retriever: BaseRetriever,
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153 |
+
multi_modal_llm: OpenAIMultiModal,
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154 |
+
node_postprocessors: Optional[List[BaseNodePostprocessor]] = [],
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):
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super().__init__(
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157 |
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qa_prompt=qa_prompt,
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158 |
+
retriever=retriever,
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159 |
+
multi_modal_llm=multi_modal_llm,
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160 |
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node_postprocessors=node_postprocessors
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161 |
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)
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162 |
+
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163 |
+
def custom_query(self, query_str: str):
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164 |
+
# retrieve most relevant nodes
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165 |
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nodes = self.retriever.retrieve(query_str)
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166 |
+
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167 |
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# create image nodes from the image associated with those nodes
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+
image_nodes = [
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169 |
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NodeWithScore(node=ImageNode(image_path=n.node.metadata["image_path"]))
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170 |
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for n in nodes
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171 |
+
]
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172 |
+
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173 |
+
# create context string from parsed markdown text
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174 |
+
ctx_str = "\n\n".join(
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175 |
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[r.node.get_content(metadata_mode=MetadataMode.LLM).strip() for r in nodes]
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+
)
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177 |
+
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178 |
+
# prompt for the LLM
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179 |
+
fmt_prompt = self.qa_prompt.format(
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180 |
+
context_str=ctx_str, query_str=query_str, encoded_image_url=encoded_image_url
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181 |
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)
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182 |
+
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183 |
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# use the multimodal LLM to interpret images and generate a response to the prompt
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184 |
+
llm_response = self.multi_modal_llm.complete(
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185 |
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prompt=fmt_prompt,
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186 |
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image_documents=[image_node.node for image_node in image_nodes],
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187 |
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)
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188 |
+
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189 |
+
return Response(
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190 |
+
response=str(llm_response),
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191 |
+
source_nodes=nodes,
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192 |
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metadata={"text_nodes": nodes, "image_nodes": image_nodes},
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193 |
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)
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194 |
+
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195 |
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query_engine = MultimodalQueryEngine(QA_PROMPT, retriever, gpt_4o_mm)
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196 |
+
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197 |
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response = query_engine.custom_query(query_text)
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198 |
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return response.response
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199 |
+
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200 |
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# Define Gradio interface
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201 |
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md_json_objs = []
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202 |
+
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203 |
+
def upload_wrapper(uploaded_file):
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204 |
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global md_json_objs
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205 |
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md_json_objs = upload_and_process_file(uploaded_file)
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return "File successfully processed!"
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207 |
+
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208 |
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iface = gr.Interface(
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209 |
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fn=ask_question,
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210 |
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inputs=[
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gr.inputs.State(),
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212 |
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gr.inputs.Textbox(label="Enter your query:"),
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213 |
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gr.inputs.File(label="Upload a query image (if any):", optional=True)
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],
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outputs="text",
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title="Medical Knowledge Base & Query System"
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)
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218 |
+
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219 |
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upload_iface = gr.Interface(
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220 |
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fn=upload_wrapper,
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inputs=gr.inputs.File(label="Upload a medical textbook (pdf):"),
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222 |
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outputs="text",
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223 |
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title="Upload Knowledge Base"
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)
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225 |
+
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226 |
+
app = gr.TabbedInterface([upload_iface, iface], ["Upload Knowledge Base", "Ask a Question"])
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227 |
+
app.launch()
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