Initial commit of Gradio chatbot

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+chroma_db/chroma.sqlite3 filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,8 +1,8 @@
 ---
-title: LLM Chatbot2
-emoji: 🔥
-colorFrom: gray
-colorTo: red
+title: LLM
+emoji: 📈
+colorFrom: blue
+colorTo: gray
 sdk: gradio
 sdk_version: 5.47.2
 app_file: app.py

app.py

@@ -0,0 +1,144 @@
+import os
+import gradio as gr
+from langchain_community.document_loaders import TextLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import Chroma
+from langchain_community.llms import HuggingFacePipeline
+from langchain.chains import ConversationalRetrievalChain
+from langchain.memory import ConversationBufferMemory
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline
+
+# -------------------------------------------------------------------
+# Constants
+DB_DIR = "chroma_db"
+MODEL_NAME_EMBEDDINGS = "sentence-transformers/all-MiniLM-L6-v2"
+MODEL_ID_LLM = "google/flan-t5-base"
+DOC_PATH = "temp_docs/samsung_manual.txt"  # fixed document path
+
+# Globals
+conversation_chain = None
+chat_history = []  # [{"role": "user/assistant", "content": "..."}]
+
+# -------------------------------------------------------------------
+def load_and_process_document():
+    """Load the Samsung manual, split it, embed it, and create vectorstore."""
+    if not os.path.exists(DOC_PATH):
+        raise FileNotFoundError(f"❌ Document not found at: {DOC_PATH}")
+
+    print("📄 Loading document...")
+    # Force UTF-8 encoding to handle special characters
+    loader = TextLoader(DOC_PATH, encoding="utf-8")
+    docs = loader.load()
+
+    print("✂️ Splitting document into chunks...")
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=200)
+    texts = text_splitter.split_documents(docs)
+
+    print("🧠 Creating embeddings...")
+    embeddings = HuggingFaceEmbeddings(model_name=MODEL_NAME_EMBEDDINGS)
+
+    print("💾 Building Chroma vectorstore...")
+    vectorstore = Chroma.from_documents(
+        documents=texts,
+        embedding=embeddings,
+        persist_directory=DB_DIR
+    )
+    return vectorstore, len(texts)  # return number of chunks
+
+
+def get_conversational_chain(vectorstore):
+    """Create the conversational retrieval chain."""
+    tokenizer = AutoTokenizer.from_pretrained(MODEL_ID_LLM)
+    model = AutoModelForSeq2SeqLM.from_pretrained(MODEL_ID_LLM)
+    pipe = pipeline(
+        "text2text-generation",
+        model=model,
+        tokenizer=tokenizer,
+        max_length=512,
+        temperature=0.1,
+        top_p=0.95,
+        repetition_penalty=1.2
+    )
+    llm = HuggingFacePipeline(pipeline=pipe)
+
+    memory = ConversationBufferMemory(
+        memory_key="chat_history",
+        return_messages=True
+    )
+
+    chain = ConversationalRetrievalChain.from_llm(
+        llm=llm,
+        retriever=vectorstore.as_retriever(search_kwargs={"k": 2}),
+        memory=memory
+    )
+    return chain
+
+def chatbot_response(user_input):
+    """Generate chatbot response from conversation chain."""
+    global conversation_chain, chat_history
+
+    if conversation_chain is None:
+        chat_history.append({
+            "role": "assistant",
+            "content": "⚠️ The chatbot is not ready. Please check the server logs."
+        })
+        return chat_history
+
+    chat_history.append({"role": "user", "content": user_input})
+    response = conversation_chain({"question": user_input})
+    ai_answer = response["answer"]
+    chat_history.append({"role": "assistant", "content": ai_answer})
+    return chat_history
+
+# -------------------------------------------------------------------
+# Gradio Interface
+with gr.Blocks() as demo:
+    gr.Markdown("## 🤖 Chat with Samsung Manual")
+    gr.Markdown("Ask questions about the **Samsung Manual** document below:")
+
+    # Status info
+    status_md = gr.Markdown("⏳ Initializing chatbot...")
+
+    # Chat interface
+    chatbot = gr.Chatbot(label="Conversation", type="messages")
+    user_input = gr.Textbox(
+        label="Type your question here…",
+        placeholder="Ask me about the Samsung manual..."
+    )
+    submit_btn = gr.Button("Ask")
+
+    # -------------------------------------------------------------------
+    # Initialization function to show status
+    def init_chatbot():
+        global conversation_chain
+        try:
+            if not os.path.exists(DB_DIR) or not os.listdir(DB_DIR):
+                # Rebuild vectorstore
+                vectorstore, chunks = load_and_process_document()
+                msg = f"✅ Manual processed and stored! Total chunks: {chunks}"
+            else:
+                embeddings = HuggingFaceEmbeddings(model_name=MODEL_NAME_EMBEDDINGS)
+                vectorstore = Chroma(persist_directory=DB_DIR, embedding_function=embeddings)
+                chunks = len(vectorstore._collection.get()["metadatas"])
+                msg = f"✅ Chroma DB loaded! Total chunks: {chunks}"
+
+            conversation_chain = get_conversational_chain(vectorstore)
+            return msg
+        except Exception as e:
+            import traceback
+            traceback.print_exc()
+            return f"❌ Failed to initialize chatbot: {e}"
+
+    # Initialize on startup
+    status_md.value = init_chatbot()
+
+    submit_btn.click(
+        fn=chatbot_response,
+        inputs=user_input,
+        outputs=chatbot
+    )
+
+# -------------------------------------------------------------------
+if __name__ == "__main__":
+    demo.launch()

chroma_db/54b1f93e-ee6f-4cfc-851a-34051bcd606f/data_level0.bin

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chroma_db/54b1f93e-ee6f-4cfc-851a-34051bcd606f/header.bin

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chroma_db/54b1f93e-ee6f-4cfc-851a-34051bcd606f/length.bin

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chroma_db/chroma.sqlite3

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requirements.txt

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+pypdf
+gradio 
+langchain 
+chromadb 
+sentence-transformers 
+transformers 
+torch