Delete 2_Image_QA.py

2_Image_QA.py

@@ -1,160 +0,0 @@
-import streamlit as st
-from langchain.schema import Document
-from langchain.text_splitter import RecursiveCharacterTextSplitter
-from langchain.vectorstores import FAISS
-from langchain_community.docstore import InMemoryDocstore
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_core.messages import HumanMessage
-from langchain_cerebras import ChatCerebras
-from langchain_mistralai import ChatMistralAI
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain.prompts import ChatPromptTemplate
-from langchain.schema import StrOutputParser
-from uuid import uuid4
-import faiss
-import os
-from dotenv import load_dotenv
-import logging
-import httpx
-import base64
-import asyncio
-
-# Initialize environment variables and logging
-load_dotenv()
-logging.basicConfig(level=logging.INFO)
-logger = logging.getLogger(__name__)
-
-# Async function to invoke chain
-async def async_invoke_chain(chain, input_data):
-    loop = asyncio.get_event_loop()
-    return await loop.run_in_executor(None, chain.invoke, input_data)
-
-# Initialize session state for messages and models
-if "messages" not in st.session_state:
-    st.session_state.messages = []
-
-if "models" not in st.session_state:
-    st.session_state.models = {
-        "Gemini": ChatGoogleGenerativeAI(
-            model="gemini-2.0-flash-exp",
-            temperature=0.8,
-            verbose=True,
-            api_key=os.getenv("GOOGLE_AI_STUDIO_API_KEY")
-        ),
-        "Mistral": ChatMistralAI(
-            model_name="open-mistral-nemo",
-            temperature=0.8,
-            verbose=True
-        ),
-        "Llama": ChatCerebras(
-            model="llama-3.3-70b",
-            temperature=0.8,
-            verbose=True,
-            api_key=os.getenv("CEREBRAS_API_KEY")
-        )
-    }
-
-# Initialize embeddings model
-if "embeddings" not in st.session_state:
-    model_name = "sentence-transformers/all-mpnet-base-v2"
-    model_kwargs = {'device': 'cpu'}
-    encode_kwargs = {'normalize_embeddings': False}
-    st.session_state.embeddings = HuggingFaceEmbeddings(
-        model_name=model_name,
-        model_kwargs=model_kwargs,
-        encode_kwargs=encode_kwargs
-    )
-
-st.header("📸📈📊 ֎ Image Content Analysis and Question Answering")
-
-# Brief overview for image content analysis
-description = """
-Upload an image, and the AI will analyze its content and answer your questions. 
-It can interpret various types of images including:
-- General imagery (objects, people, scenes)
-- Diagrams, graphs, and data visualizations
-- Scientific and medical images
-- Text-based images (documents, screenshots)
-"""
-
-# Display the brief description
-st.write(description)
-
-# File upload and URL input
-st.header("Upload Image for Question Answering")
-uploaded_file = st.file_uploader("Upload an image (.jpeg, .jpg, .png, etc.):", type=["jpeg", "jpg", "png"])
-
-st.header("Or Enter the Image URL :")
-image_url = st.text_input("Enter the image URL")
-
-image_data = None
-
-if uploaded_file:
-    st.image(uploaded_file, caption="Uploaded Image", use_column_width=True)
-    image_data = base64.b64encode(uploaded_file.read()).decode("utf-8")
-elif image_url:
-    try:
-        with httpx.Client() as client:
-            response = client.get(image_url)
-            response.raise_for_status()
-            st.image(response.content, caption="Image from URL", use_column_width=True)
-            image_data = base64.b64encode(response.content).decode("utf-8")
-    except Exception as e:
-        st.error(f"Error fetching image from URL: {e}")
-
-if image_data:
-    message = HumanMessage(content=[{
-            "type": "text", "text": "Describe what is in the image in detail."
-        }, {
-            "type": "image_url", "image_url": {"url": f"data:image/jpeg;base64,{image_data}"}
-        }])
-
-    # Generate response from the model
-    response = asyncio.run(async_invoke_chain(st.session_state.models["Gemini"], [message]))
-    knowledge = [Document(page_content=response.content)]
-
-    # Split text into chunks for indexing
-    text_splitter = RecursiveCharacterTextSplitter(separators="\n\n", chunk_size=1500, chunk_overlap=200)
-    chunks = text_splitter.split_documents(knowledge)
-
-    # Create FAISS IndexHNSWFlat for indexing image embeddings
-    index = faiss.IndexFlatL2(len(st.session_state.embeddings.embed_query("hello world")))
-
-    # Create FAISS vector store for document retrieval
-    vector_store = FAISS(
-        embedding_function=st.session_state.embeddings,
-        index=index,
-        docstore=InMemoryDocstore(),
-        index_to_docstore_id={},
-    )
-
-    # Generate unique IDs and add documents to the store
-    ids = [str(uuid4()) for _ in range(len(chunks))]
-    vector_store.add_documents(documents=chunks, ids=ids)
-
-    # Update the mapping between FAISS index and document IDs
-    for idx, doc_id in enumerate(ids):
-        vector_store.index_to_docstore_id[idx] = doc_id
-
-    # Create image retriever with the FAISS index
-    image_retriever = vector_store.as_retriever(search_type="similarity", search_kwargs={"k": 6})
-
-    def get_retrieved_context(query):
-        retrieved_documents = image_retriever.get_relevant_documents(query)
-        return "\n".join(doc.page_content for doc in retrieved_documents)
-
-    # User query for image QA
-    user_input = st.chat_input("Ask a question about the image:")
-
-    prompt = ChatPromptTemplate.from_messages([(
-            "system", "You are an expert in analyzing images. Use the context: {context} to answer the query."
-        ), ("human", "{question}")])
-
-    if user_input:
-        st.session_state.messages.append({"role": "user", "content": user_input})
-        qa_chain = prompt | st.session_state.models["Mistral"] | StrOutputParser()
-        context = get_retrieved_context(user_input)
-        response_message = asyncio.run(async_invoke_chain(qa_chain, {"question": user_input, "context": context}))
-        st.session_state.messages.append({"role": "assistant", "content": response_message})
-        for message in st.session_state.messages:
-            st.chat_message(message["role"]).markdown(message["content"])