Upload 3 files

app.py

@@ -0,0 +1,193 @@
+import os
+import zipfile
+import chromadb
+import gradio as gr
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.output_parsers import StrOutputParser
+from langchain_together import ChatTogether
+from langchain_community.vectorstores import Chroma
+from langchain_community.embeddings import HuggingFaceEmbeddings
+
+# Log: Check if chroma_store exists
+if not os.path.exists("chroma_store"):
+    print("🔍 chroma_store folder not found. Attempting to unzip...")
+    try:
+        with zipfile.ZipFile("chroma_store.zip", "r") as zip_ref:
+            zip_ref.extractall("chroma_store")
+        print("✅ Successfully extracted chroma_store.zip.")
+    except Exception as e:
+        print(f"❌ Failed to unzip chroma_store.zip: {e}")
+else:
+    print("✅ chroma_store folder already exists. Skipping unzip.")
+
+# ChromaDB setup
+chroma_client = chromadb.PersistentClient(path="./chroma_store")
+embedding_function = HuggingFaceEmbeddings(model_name="BAAI/bge-base-en-v1.5")
+vectorstore = Chroma(
+    client=chroma_client,
+    collection_name="imageonline_chunks",
+    embedding_function=embedding_function
+)
+
+# Retriever setup (k=5)
+retriever = vectorstore.as_retriever(search_kwargs={"k": 5, "filter": {"site": "imageonline"}})
+
+# Updated retrieval logic: return full concatenated context and top 2 references
+def retrieve_with_metadata(query, k=5, max_refs=2):
+    docs = retriever.get_relevant_documents(query)
+    if not docs:
+        return {
+            "context": "No relevant context found.",
+            "references": []
+        }
+
+    # Join all documents for LLM input
+    context = "\n\n".join(doc.page_content for doc in docs)
+
+    # Unique references (max 2)
+    seen = set()
+    references = []
+    for doc in docs:
+        source = doc.metadata.get("source", "Unknown")
+        section = doc.metadata.get("section", "Unknown")
+        key = (section, source)
+        if key not in seen:
+            seen.add(key)
+            references.append({"section": section, "source": source})
+        if len(references) >= max_refs:
+            break
+
+    return {
+        "context": context,
+        "references": references
+    }
+
+# LLM initialization
+llm = ChatTogether(
+    model="meta-llama/Llama-3-8b-chat-hf",
+    temperature=0.3,
+    max_tokens=1024,
+    top_p=0.7,
+    together_api_key="a36246d65d8290f43667350b364c5b6bb8562eb50a4b947eec5bd7e79f2dffc6"
+)
+
+# Improved Prompt Template
+prompt = ChatPromptTemplate.from_template("""
+You are a knowledgeable assistant for ImageOnline Pvt. Ltd. .
+
+Answer the user's query using ONLY the following context extracted from our official website.
+
+If the answer is not clearly present in the context, say "I couldn't find the information on the site."
+
+--------------------
+{context}
+--------------------
+
+Query: {question}
+""")
+
+# RAG chain
+rag_chain = (
+    {
+        "context": lambda x: retrieve_with_metadata(x)["context"],
+        "question": RunnablePassthrough()
+    }
+    | prompt
+    | llm
+    | StrOutputParser()
+)
+
+# References for display
+def get_references(query):
+    return retrieve_with_metadata(query)["references"]
+
+
+from datetime import datetime
+import time
+import gradio as gr
+
+# Chat function
+def chat_interface(message, history):
+    history = history or []
+
+    timestamp_user = datetime.now().strftime("%H:%M:%S")
+    user_msg = f"🧑 **You**\n{message}\n\n<span style='font-size: 0.8em; color: gray;'>⏱️ {timestamp_user}</span>"
+
+    bot_msg = "⏳ _Bot is typing..._"
+    history.append((user_msg, bot_msg))
+
+    try:
+        time.sleep(0.5)
+        answer = rag_chain.invoke(message)
+        references = get_references(message)
+
+        if references:
+            ref_lines = "\n".join(f"{ref['section']} – {ref['source']}" for ref in references)
+            ref_string = f"\n\n📚 **Reference(s):**\n{ref_lines}"
+        else:
+            ref_string = "\n\n📚 **Reference(s):**\n_None available_"
+
+        full_response = answer.strip() + ref_string
+        timestamp_bot = datetime.now().strftime("%H:%M:%S")
+        bot_msg = f"🤖 **Bot**\n{full_response}\n\n<span style='font-size: 0.8em; color: gray;'>⏱️ {timestamp_bot}</span>"
+
+        history[-1] = (user_msg, bot_msg)
+
+    except Exception as e:
+        timestamp_bot = datetime.now().strftime("%H:%M:%S")
+        error_msg = f"🤖 **Bot**\n⚠️ {str(e)}\n\n<span style='font-size: 0.8em; color: gray;'>⏱️ {timestamp_bot}</span>"
+        history[-1] = (user_msg, error_msg)
+
+    return history, history, ""
+
+# Gradio Launcher
+def launch_gradio():
+    with gr.Blocks(css="""
+    .gr-button {
+        background-color: orange !important;
+        color: white !important;
+        font-weight: bold;
+        border-radius: 6px !important;
+        border: 1px solid darkorange !important;
+    }
+
+    .gr-button:hover {
+        background-color: darkorange !important;
+    }
+
+    .gr-textbox textarea {
+        border: 2px solid orange !important;
+        border-radius: 6px !important;
+        padding: 0.75rem !important;
+        font-size: 1rem;
+    }
+    """) as demo:
+
+        # Header and Subtitle
+        gr.Markdown("# 💬 ImageOnline RAG Chatbot")
+        gr.Markdown("Welcome! Ask about Website Designing, Web Development, App Development, About Us, Digital Marketing etc.")
+
+        chatbot = gr.Chatbot()
+        state = gr.State([])
+
+        with gr.Row(equal_height=True):
+            msg = gr.Textbox(
+                placeholder="Ask your question here...",
+                show_label=False,
+                scale=9
+            )
+            send_btn = gr.Button("🚀 Send", scale=1)
+
+        msg.submit(chat_interface, inputs=[msg, state], outputs=[chatbot, state, msg])
+        send_btn.click(chat_interface, inputs=[msg, state], outputs=[chatbot, state, msg])
+
+        with gr.Row():
+            clear_btn = gr.Button("🧹 Clear Chat")
+            clear_btn.click(fn=lambda: ([], []), outputs=[chatbot, state])
+
+    return demo
+
+# Launch the app
+demo = launch_gradio()
+demo.launch()

chroma_store.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:66e8b84ee00bcc26367fe813d3759368e2a24d5509a2cc26c4cbf20c39dfed5d
+size 822068

requirements

@@ -0,0 +1,12 @@
+langchain
+langchain-huggingface
+langchain-together
+langchain-community
+chromadb
+sentence-transformers
+trafilatura
+beautifulsoup4
+nltk
+tiktoken
+gradio
+together