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+Dataset/Drama/Broken_images.pdf filter=lfs diff=lfs merge=lfs -text
+Dataset/Drama/Chandalika.pdf filter=lfs diff=lfs merge=lfs -text
+Dataset/non_fiction/Flim_making.pdf filter=lfs diff=lfs merge=lfs -text
+Dataset/non_fiction/Freedom_freedom.pdf filter=lfs diff=lfs merge=lfs -text
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+Dataset/Poems/Kubla_khan.pdf filter=lfs diff=lfs merge=lfs -text
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+Dataset/Stories/I_sell_my_dreams.pdf filter=lfs diff=lfs merge=lfs -text
+Dataset/Stories/One_centimetre.pdf filter=lfs diff=lfs merge=lfs -text
+Dataset/Stories/Tomorrow.pdf filter=lfs diff=lfs merge=lfs -text
+qdrant/a_wedding_in_brownsville/collection/A[[:space:]]Wedding[[:space:]]In[[:space:]]Brownsville/storage.sqlite filter=lfs diff=lfs merge=lfs -text
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+qdrant/chandalika/collection/Chandalika/storage.sqlite filter=lfs diff=lfs merge=lfs -text
+qdrant/eveline/collection/Eveline/storage.sqlite filter=lfs diff=lfs merge=lfs -text
+qdrant/film_making/collection/Flim[[:space:]]Making/storage.sqlite filter=lfs diff=lfs merge=lfs -text
+qdrant/freedom/collection/Freedom/storage.sqlite filter=lfs diff=lfs merge=lfs -text
+qdrant/i_sell_my_dreams/collection/I[[:space:]]Sell[[:space:]]My[[:space:]]Dreams/storage.sqlite filter=lfs diff=lfs merge=lfs -text
+qdrant/on_science_fiction/collection/On[[:space:]]Science[[:space:]]Fiction/storage.sqlite filter=lfs diff=lfs merge=lfs -text
+qdrant/one_centimeter/collection/One[[:space:]]Centimeter/storage.sqlite filter=lfs diff=lfs merge=lfs -text
+qdrant/the_argumentative_indian/collection/The[[:space:]]Argumentative[[:space:]]Indian/storage.sqlite filter=lfs diff=lfs merge=lfs -text
+qdrant/the_mark_on_the_wall/collection/The[[:space:]]Mark[[:space:]]On[[:space:]]The[[:space:]]Wall/storage.sqlite filter=lfs diff=lfs merge=lfs -text
+qdrant/tomorrow/collection/Tomorrow/storage.sqlite filter=lfs diff=lfs merge=lfs -text
+qdrant/why_the_novel_matters/collection/Why[[:space:]]The[[:space:]]Novel[[:space:]]Matters/storage.sqlite filter=lfs diff=lfs merge=lfs -text

Dataset/Drama/Broken_images.pdf

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README.md

@@ -1,12 +1,14 @@
 ---
-title: RAG Doc Chatbot
-emoji: 📚
+title: KaleidoscopeChat
+emoji: 💬
 colorFrom: yellow
 colorTo: purple
 sdk: gradio
-sdk_version: 5.44.1
+sdk_version: 5.0.1
 app_file: app.py
 pinned: false
+license: mit
+short_description: 'KaleidoscopChat answers queries: NCERT 12th Textbook.'
 ---
 
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+An example chatbot using [Gradio](https://gradio.app), [`huggingface_hub`](https://huggingface.co/docs/huggingface_hub/v0.22.2/en/index), and the [Hugging Face Inference API](https://huggingface.co/docs/api-inference/index).

app.py

@@ -0,0 +1,181 @@
+import os
+import gradio as gr
+#from langchain_openai import ChatOpenAI
+from langchain_groq import ChatGroq
+from langchain_core.runnables import Runnable
+from chat_engine import conversation_prompt
+from chat_engine import chapter_index
+from chat_engine import tree_index_list
+from chat_engine import select_index
+from langchain_core.messages import SystemMessage, HumanMessage, AIMessage
+from pathlib import Path
+#from chat_engine import prompt_query
+
+groq_llm = ChatGroq(
+    model_name="llama-3.3-70b-versatile",  
+    temperature=0.2,  
+    api_key=os.getenv('GROQ_API_KEY')
+)
+
+response_chain: Runnable = conversation_prompt | groq_llm
+
+#groq_user_engg=ChatGroq(
+ #   model_name="llama3-70b-8192",
+  #  temperature=0,
+   # api_key=os.getenv("GROQ_API")
+    #)
+
+#query_chain: Runnable = prompt_query | groq_user_engg
+
+def gradio_chat(user_query, chat_history, index, tree_index_list=tree_index_list, chapter_index=chapter_index,response_chain=response_chain):
+    
+    if chat_history is None:
+        chat_history=[]
+    
+    if user_query=="":
+        chat_history.append(HumanMessage(user_query))
+        chat_history.append(AIMessage("Kindly ask a question from the selected chapter."))
+        return "Kindly ask a question from the selected chapter", chat_history
+    
+    vector_index=select_index(index)
+    retriever1=vector_index.as_retriever(similarity_top_k=2)
+    retrieved_nodes1=retriever1.retrieve(user_query)
+    
+    tree_index=tree_index_list[chapter_index[retrieved_nodes1[0].metadata["chapter"]]]
+    if retrieved_nodes1[0].metadata["section"]=="poem":
+        retriever = tree_index.as_retriever(similarity_top_k=4, retriever_mode="all_leaf")
+        retrieved_nodes3=retriever.retrieve("summarize the poem")
+        #answer = response_synthesizer.synthesize(query=user_query, nodes=retrieved_nodes3)
+        pext=""
+        for content in retrieved_nodes3:
+            pext=pext+' '+content.text.strip()
+        context='Author: '+retrieved_nodes1[0].metadata['author']+'\nSection: '+retrieved_nodes1[0].metadata['section']+'\nChapter: '+retrieved_nodes1[0].metadata['chapter']+'\nContext: '+pext
+        
+    else:
+        contextt=[]
+        for text in retrieved_nodes1:
+            contextt.append((text.metadata['page'], text.text))
+        contextt.sort(key=lambda x:x[0])
+        context1=[x[1] for x in contextt]
+    
+        retriever = tree_index.as_retriever(similarity_top_k=1,retriever_mode="root", 
+                                            search_kwargs={"num_children":3})
+
+        retrieved_nodes2=retriever.retrieve("summarize this chapter")
+        
+        for text in retrieved_nodes2:
+            context1.append(text.text.strip())
+        context="\n".join(context1)
+        context='Author: '+retrieved_nodes1[0].metadata['author']+'\nSection: '+retrieved_nodes1[0].metadata['section']+'\nChapter: '+retrieved_nodes1[0].metadata['chapter']+'\nContext: '+context
+
+    chat_history.append(HumanMessage(user_query))
+    response=response_chain.invoke({"chat_history":chat_history[-12:], "user_query":user_query, "document_context":context})
+    chat_history.append(AIMessage(response.content))
+    
+    return response.content, chat_history
+
+#def prompt_engg(message, chain_history, index, previous_index="Broken Images"):
+    
+    #if previous_index == index:
+        #index_change=0 
+    #else:
+        #index_change=1
+    #question=query_chain.invoke({"user_query":message, "chat_history":chain_history, "index":index, "index_change":index_change})
+    
+    #return question.content
+
+def respond(message, chain_history, ui_history, index):
+
+    ui_history.append({"role": "user", "content": message})
+    #message=prompt_engg(message, chain_history, index)
+    response_text, updated_history = gradio_chat(message, chain_history, index=index)
+    
+    if ui_history is None:
+        ui_history = []
+    
+    ui_history.append({"role": "assistant", "content": response_text})
+    #print(ui_history)
+    return "", updated_history, ui_history
+
+def download_file(index):
+    filepath=chapter_dir[index]
+    return filepath
+
+custom_css = """
+#chatbot_interface {
+    background: #f0f0f0;
+    padding: 20px;
+    border-radius: 10px;
+}
+
+/* Center the markdown text */
+#welcome_markdown {
+    text-align: center;
+    margin: auto;
+}
+"""
+
+with gr.Blocks(css=custom_css,fill_width=True) as demo:
+    gr.Markdown("""
+    # I’m Shalini ☺️ # 
+    Your Creative Muse — Where Literature Dances, Art Breathes, and Philosophy Whispers 🎨📖🪞  
+
+    Welcome to *Kaleidoscope* —  
+    Where words don’t just sit still — they swirl, they shimmer, they *sing*.   
+    Have a question from the 12th NCERT English textbook *Kaleidoscope*?  
+    Ask — and I’ll reply with words that wander, wonder, and land like truth. 📚🌿 
+    ---
+    Let’s begin this soulful journey together:
+    1. Pick your chapter from the dropdown below.  
+    2. Step into the story with your question.  
+    3. I’ll craft a reply — rhythmic, radiant, and rich with meaning. 🖼️💫
+    """,elem_id="welcome_markdown")
+    
+    chapter_dir={"Broken Images":"Dataset/Drama/Broken_images.pdf", 
+                 "Blood":"Dataset/Poems/Blood.pdf", 
+                 "Flim Making":"Dataset/non_fiction/Flim_making.pdf", 
+                 "Kubla Khan":"Dataset/Poems/Kubla_khan.pdf", 
+                 "One Centimeter":"Dataset/Stories/One_centimetre.pdf",
+              "I Sell My Dreams":"Dataset/Stories/I_sell_my_dreams.pdf", 
+              "Poems By Blake":"Dataset/Poems/The_divine_image.pdf", 
+              "Time and Time Again":"Dataset/Poems/Time_and_time_again.pdf", 
+              "On Time":"Dataset/Poems/On_time.pdf",
+              "Trees":"Dataset/Poems/Trees_emily_dickinson.pdf", 
+              "On Science Fiction":"Dataset/non_fiction/On_science_fiction.pdf", 
+              "The Argumentative Indian":"Dataset/non_fiction/The_argumentative_indian.pdf", 
+              "Why The Novel Matters":"Dataset/non_fiction/Why_the_novel_matters.pdf",
+              "Tomorrow":"Dataset/Stories/Tomorrow.pdf", 
+              "A Lecture Upon The Shadow":"Dataset/Poems/A_lecture_upon_the_shadow.pdf", 
+              "Freedom":"Dataset/non_fiction/Freedom_freedom.pdf", 
+              "A Wedding in Brownsville":"Dataset/Stories/A_wedding_in_brownsville.pdf",
+              "Eveline":"Dataset/Stories/eveline.pdf", 
+              "Chandalika":"Dataset/Drama/Chandalika.pdf", 
+              "The Wild Swans At Coole":"Dataset/Poems/The_wild_swans_at_coole.pdf", 
+              "The Mark On The Wall":"Dataset/non_fiction/The_mark_on_the_wall.pdf"}
+    
+    chatbot = gr.Chatbot(label="Chat Interface", elem_id="chatbot_interface", type="messages")
+    
+    index=gr.State()
+    with gr.Row():
+        index=gr.Dropdown(
+            choices=list(chapter_dir.keys()),
+            label="Chapter",
+            value="Broken Images",
+            info="Select the chapter on which you would like to ask questions."
+        )
+        msg = gr.Textbox(label="Enter your query:", placeholder="Type your question here...", lines=2)
+        
+        d = gr.DownloadButton("Download Selected Chapter", visible=True)
+        index.change(fn=download_file, inputs=index, outputs=d)
+        #d.click(download_file, index, [d])
+    
+    chain_history = gr.State([])  # For LangChain message objects
+    ui_history = gr.State([])       # For display, a list of dictionaries
+    
+    gr.Button("Glide In🎨").click(respond, [msg, chain_history, ui_history, index], [msg, chain_history, chatbot])
+    
+    #clear = gr.ClearButton([msg, chatbot], size="sm")
+    msg.submit(fn=respond, inputs=[msg, chain_history, ui_history, index], outputs=[msg, chain_history, chatbot])
+
+demo.launch(allowed_paths=["Dataset/Stories","Dataset/Drama",
+                           "Dataset/Poems","Dataset/non_fiction"])

chapter_index.pkl

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chat_engine.py

@@ -0,0 +1,45 @@
+import pickle
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import Runnable
+from langchain_openai import ChatOpenAI
+from timeit import default_timer as timer
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from qdrant_client import QdrantClient
+from llama_index.vector_stores.qdrant import QdrantVectorStore
+from llama_index.core import StorageContext, VectorStoreIndex
+
+with open("custom_prompt.pkl", "rb") as file:
+    custom_template=pickle.load(file)
+    
+with open("tree_index_list.pkl","rb") as f:
+    tree_index_list=pickle.load(f)
+
+with open("chapter_index.pkl","rb") as f:
+    chapter_index=pickle.load(f)
+    
+with open("index_dict.pkl", "rb") as file:
+    index_dict=pickle.load(file)
+
+conversation_prompt=ChatPromptTemplate.from_template(template=custom_template)
+    
+#with open("prompt_engg.pkl", "rb") as file:
+#    prompt_engg=pickle.load(file)
+
+#prompt_query=ChatPromptTemplate.from_template(template=prompt_engg)
+
+embedding_function = HuggingFaceEmbedding(model_name="sentence-transformers/all-roberta-large-v1")
+
+def select_index(index, index_dict=index_dict, embedding_function=embedding_function):
+    for key in index_dict:
+        if index==key:
+        
+            qdrant_local_path = index_dict[key]
+            qdrant_client = QdrantClient(path=qdrant_local_path)
+            collection_name = key
+            vector_size = 1024
+            qdrant_vector_store = QdrantVectorStore(client=qdrant_client, collection_name=collection_name)
+            storage_context = StorageContext.from_defaults(vector_store=qdrant_vector_store)
+            vector_index = VectorStoreIndex.from_vector_store(vector_store=qdrant_vector_store, embed_model=embedding_function)
+            
+            return vector_index
+

custom_prompt.pkl

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