Upload folder using huggingface_hub

.gitignore

@@ -0,0 +1,7 @@
+__pycache__/
+*.pkl
+.env
+.venv
+hf_cache/
+.github/
+.python-version

README.md

@@ -1,12 +1,22 @@
----
-title: YourHonor
-emoji: 📊
-colorFrom: yellow
-colorTo: gray
-sdk: gradio
-sdk_version: 6.3.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+---
+title: YourHonor
+app_file: app.py
+sdk: gradio
+sdk_version: 4.44.0
+---
+# 🇮🇳 Constitution of India RAG Chatbot
+
+Production-grade Q&A over full Constitution text using **Phi-3-mini + LlamaIndex + ChromaDB**.
+
+## Features
+- ✅ Semantic search (handles typos: "Artical 14" → Article 14)
+- ✅ Chat history (auto-truncated)
+- ✅ Citations & hallucination-proof
+- ✅ Edge cases: "Not found" responses
+- ✅ HF Spaces CPU-Basic (~4GB RAM)
+
+## Deploy HF Spaces
+1. Push all files (incl. `chroma_db/`)
+2. Create Gradio Space → Auto-builds!
+
+**Try:** "Article 21 fundamental right?" → Perfect answer + context!

app.py

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+""" Interactive RAG chatbot using Gradio.
+Name: Constitution Of India RAG Chatbot
+Phi3-mini-4k + MiniLM + chromaDB
+ """
+
+import os
+from pathlib import Path
+from turtle import undo
+import gradio as gr
+from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, PromptTemplate,Settings
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.llms.huggingface import HuggingFaceLLM
+from llama_index.core.memory import ChatMemoryBuffer
+from textblob import TextBlob
+from typing import List, Tuple
+from transformers import AutoTokenizer
+
+EMBED_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
+CHROMA_DB_PATH = "./chroma_db"
+MODEL_NAME = "microsoft/Phi-3-mini-4k-instruct"
+MODEL_NAME = "Qwen/Qwen2-1.5B-Instruct"  # ✅ No tokenizer bugs
+MAX_HISTORY_TOKENS = 8000
+TOP_K = 4
+
+# user query embeded with this model
+Settings.embed_model = HuggingFaceEmbedding(model_name=EMBED_MODEL, device="cpu")
+# phi3 LLm (downloads ~2GB on first use)
+# Model name and its tokenizer name are the same most of the times. check HF for tokenizer name if not found.
+
+def only_in_case_phi3_model_loading():
+    # Pre-initialize tokenizer to ensure pad_token is set correctly
+    tokenizer = AutoTokenizer.from_pretrained(
+        MODEL_NAME,
+        trust_remote_code=True,
+        padding_side="left"
+    )
+    # Ensure pad_token is set for Phi-3
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+        tokenizer.pad_token_id = tokenizer.eos_token_id
+    
+    # Create HuggingFaceLLM - try with tokenizer parameter first
+    try:
+        llm = HuggingFaceLLM(
+            model_name=MODEL_NAME,
+            tokenizer_name=MODEL_NAME,
+            context_window=4000,
+            max_new_tokens=512,
+            device_map="cpu",
+            model_kwargs={
+                "trust_remote_code": True,
+                "low_cpu_mem_usage": True,
+                "use_safetensors": True
+            },
+            tokenizer=tokenizer  # Passing tokenizer avoids init error, but may fail later if not properly supported
+        )
+    except (TypeError, ValueError):
+        # If tokenizer parameter not supported, use workaround with __dict__
+        llm = HuggingFaceLLM(
+            model_name=MODEL_NAME,
+            tokenizer_name=MODEL_NAME,
+            context_window=4000,
+            max_new_tokens=512,
+            device_map="cpu",
+            model_kwargs={
+                "trust_remote_code": True,
+                "low_cpu_mem_usage": True,
+                "use_safetensors": True
+            },
+            tokenizer_kwargs={
+                "trust_remote_code": True,
+                "padding_side": "left"
+            }
+        )
+        # Bypass Pydantic's __setattr__ to set internal tokenizer attribute
+        object.__setattr__(llm, '_tokenizer', tokenizer)
+    
+    return llm
+
+# llm = only_in_case_phi3_model_loading()
+if (1==1):
+    llm = HuggingFaceLLM(
+    model_name=MODEL_NAME,
+    tokenizer_name=MODEL_NAME,
+    context_window=32768,
+    max_new_tokens=512,
+    device_map="cpu")
+
+qa_prompt = PromptTemplate(
+    """<|im_start|>system
+        You are an expert on the Constitution of India. Your job is to answer questions using ONLY the provided Constitution text excerpts.
+
+        RULES (MANDATORY):
+        1. Answer using ONLY the context provided below
+        2. If the answer is NOT in the context, respond EXACTLY: "❌ Not found in Constitution of India"
+        3. Cite specific Article/Section numbers when possible
+        4. Never use external knowledge or general facts
+        5. Be precise, legal, and constitutional in tone
+        6. If asked about creator - Respond with Abhijeet M
+
+        CONTEXT FROM CONSTITUTION:
+        {context_str}
+
+        QUESTION: {query_str}<|im_end|>
+        <|im_start|>assistant"""
+)
+
+class ConstitutionRAGChatBot:
+    def __init__(self):
+        if not os.path.exists(CHROMA_DB_PATH):
+            raise FileNotFoundError(f"ChromaDB index not found at {CHROMA_DB_PATH}. Run indexing first.")
+        
+        # load index from storage -- already computed by index_builder.py
+        storage_context = StorageContext.from_defaults(persist_dir=CHROMA_DB_PATH)
+        self.index = load_index_from_storage(storage_context)
+
+        self.query_engine = self.index.as_query_engine(llm=llm, chat_mode=True, similarity_top_k=TOP_K, response_mode="compact", text_qa_template=qa_prompt, memory=ChatMemoryBuffer.from_defaults(token_limit=MAX_HISTORY_TOKENS))
+
+    def preprocess_query(self, query: str) -> str:
+        """ Preprocess user query to improve accuracy. """
+        # correct spelling
+        corrected_query = TextBlob(query.strip()).correct()
+        return str(corrected_query)
+    
+    def chat(self, message: str, history: List[Tuple[str, str]]) -> str:
+        """ Callback """
+        if not message.strip():
+            return "Please, Stick to the questions regarding the Constitutions. Thanks!"
+
+        try:
+            clean_query = self.preprocess_query(message)
+            # query RAG (auto embed, retrives, generate)
+            response = self.query_engine.query(clean_query)
+
+            if "Not Found" in response.response.lower():
+                return "Its my Bad. Might be there is no information on this topic into the constitution of India or Legal language is too hard for me too.. ;)"
+            return response.response
+        except Exception as e:
+            return f"Error: {str(e)}.\n Try rephrasing your question in less workds"
+
+def create_demo():
+    rag = ConstitutionRAGChatBot()
+    demo = gr.ChatInterface(
+        fn=rag.chat,
+        title = 'YourHonor',
+        description="Ask precise questions about Articles, Rights, Duties, Amendments. ",
+        theme="soft",
+        examples=[
+            "What does Article 14 say?",
+            "Fundamental Rights list?",
+            "President election process?",
+            "Emergency provisions?",
+        ],
+        cache_examples=False,
+        retry_btn="Ask Again",
+        undo_btn="Undo",
+        submit_btn="Order!Order!"
+    )
+    return demo
+
+
+if __name__ == "__main__":
+    # Local test
+    demo = create_demo()
+    #demo.launch(server_name="0.0.0.0", server_port=7860, debug=True)
+    demo.launch(server_name="0.0.0.0", server_port=7860, share=True)

cache_download.py

@@ -0,0 +1,17 @@
+# cache_download.py - Run ONCE
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import os
+
+MODEL_NAME = "microsoft/Phi-3-mini-4k-instruct"
+MODEL_NAME = "Qwen/Qwen2-1.5B-Instruct"  # ✅ No tokenizer bugs
+
+
+print("Caching Phi-3...")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME, cache_dir="./hf_cache")
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_NAME, 
+    cache_dir="./hf_cache",
+    torch_dtype="auto",
+    device_map="cpu"
+)
+print("✅ Cached to ./hf_cache/")

chroma_db/graph_store.json

@@ -0,0 +1 @@
+{"graph_dict": {}}

chroma_db/image__vector_store.json

@@ -0,0 +1 @@
+{"embedding_dict": {}, "text_id_to_ref_doc_id": {}, "metadata_dict": {}}

chroma_db/index_store.json

@@ -0,0 +1 @@
+{"index_store/data": {"cfcece5f-fd5f-4c24-8dc8-8ffc66d51a9a": {"__type__": "vector_store", "__data__": "{\"index_id\": \"cfcece5f-fd5f-4c24-8dc8-8ffc66d51a9a\", \"summary\": null, \"nodes_dict\": {\"20d385ba-acd4-49f7-819b-4e2d437f6d5e\": \"20d385ba-acd4-49f7-819b-4e2d437f6d5e\", \"f3beb51b-972b-42f2-903f-f08e01eee71f\": \"f3beb51b-972b-42f2-903f-f08e01eee71f\", \"71cb511e-6a10-465d-8266-3c4b6bca45d7\": \"71cb511e-6a10-465d-8266-3c4b6bca45d7\", \"f2760738-2776-4bad-af1b-97c660567d34\": \"f2760738-2776-4bad-af1b-97c660567d34\", \"62785a2b-bebf-4c6e-82e7-beaccba7176c\": \"62785a2b-bebf-4c6e-82e7-beaccba7176c\", \"b9bb3564-c44d-4ecf-a714-8f703925f18d\": \"b9bb3564-c44d-4ecf-a714-8f703925f18d\", \"655f8cc5-e41b-41ef-bac0-ee29195d0baf\": \"655f8cc5-e41b-41ef-bac0-ee29195d0baf\", \"85c1759b-4dda-4f4e-9762-7ba60a190f10\": \"85c1759b-4dda-4f4e-9762-7ba60a190f10\", \"4021bbbb-b184-43fa-8714-dba450501d42\": \"4021bbbb-b184-43fa-8714-dba450501d42\", \"0f92576d-19a1-4d7d-b34d-12215c5140dc\": 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\"079f0936-7112-40e0-a633-5b4d78249b17\", \"b2abf2d2-5039-4acc-b5a8-f0bd53831581\": \"b2abf2d2-5039-4acc-b5a8-f0bd53831581\", \"4e4b29fd-1f40-463f-a913-3543391643ce\": \"4e4b29fd-1f40-463f-a913-3543391643ce\", \"9ba45ca9-a35c-47ad-b8ec-f6d4fb40aab6\": \"9ba45ca9-a35c-47ad-b8ec-f6d4fb40aab6\", \"0be5c555-fc2d-473f-8e40-969fd7a3a42a\": \"0be5c555-fc2d-473f-8e40-969fd7a3a42a\"}, \"doc_id_dict\": {}, \"embeddings_dict\": {}}"}}}

index_builder.py

@@ -0,0 +1,24 @@
+""" One time indexing: Load constitution PDF -> chunks -> embed -> store in ChromaDB
+Robust: Handles tables/images """
+
+import os
+from pathlib import Path
+from llama_index.core import Settings, VectorStoreIndex, SimpleDirectoryReader
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.core.node_parser import SimpleNodeParser
+
+Settings.embed_model = HuggingFaceEmbedding(model_name="all-MiniLM-L6-v2", 
+device="cpu")
+
+def build_index():
+    print("Building index...")
+    reader = SimpleDirectoryReader(input_dir="./data", recursive=True, file_extractor={"pdf": "llama_index.readers.file.PDFReader"})
+    documents = reader.load_data()
+    node_parser = SimpleNodeParser(chunk_size=1024, chunk_overlap=200)
+    index = VectorStoreIndex.from_documents(documents, show_progress=True, transformations=[node_parser])
+    index.storage_context.persist(persist_dir="./chroma_db")
+    print("Index built successfully!")
+
+if __name__ == "__main__":
+    build_index()
+    

main.py

@@ -0,0 +1,6 @@
+def main():
+    print("Hello from askyourhonor!")
+
+
+if __name__ == "__main__":
+    main()

pyproject.toml

@@ -0,0 +1,24 @@
+[project]
+name = "YourHonor"
+version = "0.1.0"
+description = "Indian Constitution Based RAG model"
+readme = "README.md"
+requires-python = ">=3.10"
+
+dependencies = [
+  "llama-index-core==0.10.57",
+  "llama-index-llms-huggingface==0.2.5", 
+  "llama-index-embeddings-huggingface==0.2.1",
+  "llama-index-vector-stores-chroma==0.1.10",
+  "transformers==4.41.2",
+  "torch==2.4.1",
+  "gradio==4.44.0",
+  "llama-index-readers-file==0.1.9",
+  "chromadb==0.5.3",
+  "sentence-transformers==2.7.0",
+  "accelerate==1.0.1",  # Spell correction
+  "textblob==0.18.0",
+  "watchfiles>=1.1.1",
+]
+
+