Delete .ipynb_checkpoints/demo project-checkpoint.py

.ipynb_checkpoints/demo project-checkpoint.py

@@ -1,343 +0,0 @@
-import gradio as gr
-from langchain_groq import ChatGroq
-import os
-from langgraph.graph import StateGraph, START, END
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain_chroma import Chroma
-from typing import Annotated
-from typing_extensions import TypedDict
-from pydantic import BaseModel, Field
-from langchain_core.messages import HumanMessage
-import time
-import os
-from langchain_community.document_loaders import PyPDFLoader
-from langchain_huggingface import HuggingFaceEmbeddings
-
-os.environ['GROQ_API_KEY'] = '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'
-
-class State(TypedDict):
-    query: str
-    is_safe: bool
-    is_relevant: bool
-    company_description: str
-    answer: str
-    vectorstoredb: Chroma
-
-class checker_class(BaseModel):
-    is_relevant: bool = Field(description="Check whether the given query is relevant to the company.")
-
-def invoke_llm(query):
-    llm = ChatGroq(model='llama-3.3-70b-versatile')
-    try:
-        res = llm.invoke(query)
-    except:
-        time.sleep(60)
-        res = llm.invoke(query)
-    return res.content
-    
-def invoke_relevance_checker_llm(query):
-    llm = ChatGroq(model='gemma2-9b-it')
-    checker_llm = llm.with_structured_output(checker_class)
-    try:
-        res = checker_llm.invoke([HumanMessage(content=query)])
-    except:
-        time.sleep(60)
-        res = checker_llm.invoke([HumanMessage(content=query)])
-    return res.is_relevant
-
-def safety_checker(state:State):
-    llm = ChatGroq(model='meta-llama/llama-guard-4-12b')
-    query = state['query']
-    res = llm.invoke(query)
-    if res.content == 'safe':
-        return {'is_safe':True}
-    else:
-        return {'is_safe':False, 'answer':"<SAFETY CHECKER> That prompt was harmful, please try something else"}
-        
-def relevance_checker(state:State):
-    prompt = "You are a lenient relevance-checking assistant. You will be given a user query and a company description. Your job is to decide whether the query is relevant to the company.\n✅ Approve most queries that are even loosely related.\n🚫 Only reject queries that are **clearly unrelated** or have **no connection at all**.\n\n"
-    prompt += f"\nQuery: {state['query']}"
-    prompt += f"\nDescription: {state['company_description']}"
-    res = invoke_relevance_checker_llm(prompt)
-    return {'is_relevant':res, 'answer':"Sorry! That doesn't seem to be relevant to us, please try something else."}
-
-def agent(state:State):
-    relevant_text = ""
-    search_docs = state['vectorstoredb'].similarity_search(state['query'])
-    for chunk in search_docs:
-        relevant_text += f"\n{chunk.page_content}"
-    prompt = f"You have to answer this query: {state['query']} based only on the following information: {relevant_text}. Reply only with the answer."
-    try:
-        res = invoke_llm(prompt)
-    except:
-        time.sleep(60)
-        res = invoke_llm(prompt)
-    finally:
-        return {'answer':res}
-
-def safety_assigner(state:State):
-    if state['is_safe']:
-        return 'relevant'
-    else:
-        return 'END'
-
-def relevant_assigner(state:State):
-    if state['is_relevant']:
-        return 'Agent'
-    else:
-        return 'END'
-
- def chat(query, vect, dec):
-    yield gr.update(visible=True), ""
-    mess = {'query':query, 'vectorstoredb': vect, 'company_description': dec}
-    res = graph.invoke(mess)
-    yield gr.update(visible=False), res['answer']
-
-def setter(pdf_file, description, company_name):
-    yield gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), gr.update(visible=False), "", "", ""
-    loader = PyPDFLoader(pdf_file)
-    docs = loader.load()
-    consise_pdf = docs[1].page_content if len(docs) > 1 else docs[0].page_content
-    consise_pdf = consise_pdf[:5555]
-    full_pdf = ""
-    for content in docs:
-        full_pdf += f"\n{content.page_content}"
-    embeddings = HuggingFaceEmbeddings(model_name='sentence-transformers/all-mpnet-base-v2')
-    splitter = RecursiveCharacterTextSplitter(chunk_size=400, chunk_overlap=100)
-    chunks = splitter.split_text(full_pdf)
-    vector_db = Chroma.from_texts(chunks, embeddings)    
-    prompt = "You are a company description generator assistant. "
-    prompt += "You will be given the name of a company, a short description provided by the owner, "
-    prompt += "and additional content extracted from a company file (such as a brochure or document). "
-    prompt += "Using this information, generate a concise and professional 3–4 line description of the company. Also, reply in markdown\n\n"
-    prompt += f"Company Name: {company_name}\n"
-    prompt += f"Owner's Description: {description}\n"
-    prompt += f"File Content: {consise_pdf}\n"
-    prompt += "Final Description:"
-    response = invoke_llm(prompt)
-    yield gr.update(visible=False), gr.update(visible=False), gr.update(visible=True), gr.update(visible=False), response, response, vector_db
-
-builder = StateGraph(State)
-
-builder.add_node("Safety Checker", safety_checker)
-builder.add_node("Relevance Checker", relevance_checker)
-builder.add_node("Agent", agent)
-
-builder.add_edge(START, "Safety Checker")
-builder.add_conditional_edges("Safety Checker", safety_assigner, {'relevant':"Relevance Checker", 'END': END})
-builder.add_conditional_edges("Relevance Checker", relevant_assigner, {'Agent':"Agent", 'END':END})
-builder.add_edge("Agent",END)
-
-graph = builder.compile()
-
-with gr.Blocks(css=".section {margin-bottom: 20px;}") as ui:
-
-    vectorstore_db = gr.State()
-    company_generated_description = gr.State()
-    
-    # 🌀 CSS + HTML animation injection
-    header = gr.HTML("""
-    <style>
-        .fade-in {
-            animation: fadeIn 1.2s ease-in;
-        }
-        .slide-up {
-            animation: slideUp 0.8s ease-out;
-        }
-        @keyframes fadeIn {
-            from { opacity: 0; }
-            to   { opacity: 1; }
-        }
-        @keyframes slideUp {
-            from { transform: translateY(20px); opacity: 0; }
-            to   { transform: translateY(0); opacity: 1; }
-        }
-    </style>
-    <div class='fade-in'>
-        <h1 style="text-align:center; font-size: 2.4em;">👋 Welcome to Your Personalized AI Agent Demo ✨</h1>
-        <p style="text-align:center; font-size: 1.2em;">🚀 Automate marketing, save time, and scale smartly using AI Agents</p>
-    </div>
-    """, visible=True)
-
-    with gr.Column(visible=True) as setup_page:
-        
-        with gr.Group(elem_classes=["slide-up", "section"]):
-            gr.Markdown("### 💼 What’s the name of your company/service?")
-            company_name = gr.Textbox(lines=1, placeholder="e.g., SwiftSync AI")
-
-        with gr.Group(elem_classes=["slide-up", "section"]):
-            gr.Markdown("### 📝 Tell us briefly what your company does:")
-            company_desc = gr.Textbox(lines=3, placeholder="We provide AI-driven automation tools...")
-
-        with gr.Group(elem_classes=["slide-up", "section"]):
-            gr.Markdown("### 📄 Got a business PDF? Upload it here to make your AI Agent smarter:")
-            pdf_file = gr.File(file_types=[".pdf"], label="Upload your PDF")
-
-        with gr.Group(elem_classes=["slide-up"]):
-            setup_submit = gr.Button("✨ Build My Agent Now")
-
-    with gr.Column(visible=False) as processing_page:
-        processing_msg = gr.HTML("""
-        <style>
-        @keyframes spin {
-            0%   { transform: rotate(0deg); }
-            100% { transform: rotate(360deg); }
-        }
-        @keyframes fade {
-            0%, 100% { opacity: 0.2; }
-            50% { opacity: 1; }
-        }
-        .loader {
-            border: 6px solid #e0e0e0;
-            border-top: 6px solid #00bcd4;
-            border-radius: 50%;
-            width: 50px;
-            height: 50px;
-            animation: spin 1s linear infinite;
-            box-shadow: 0 0 10px rgba(0,188,212,0.4);
-        }
-        .processing-text {
-            font-size: 1.1em;
-            margin-top: 15px;
-            font-weight: 500;
-            color: #555;
-            animation: fade 2s infinite ease-in-out;
-        }
-        </style>
-    
-        <div style="display: flex; flex-direction: column; align-items: center; margin-top: 40px;">
-            <div class="loader"></div>
-            <div class="processing-text">🧠 Building your AI Agent...</div>
-        </div>
-        """, visible=True)
-
-    with gr.Column(visible=False) as agent_page:
-        # Header Section
-        gr.HTML("""
-        <style>
-            .title-box {
-                text-align: center;
-                padding: 15px 0;
-                background: linear-gradient(90deg, #007bff 0%, #00c2ff 100%);
-                color: white;
-                border-radius: 12px;
-                box-shadow: 0 4px 10px rgba(0,0,0,0.15);
-            }
-            .info-card {
-                background: #f9f9f9;
-                border-left: 4px solid #007bff;
-                padding: 12px 20px;
-                border-radius: 8px;
-                font-size: 15px;
-                margin-bottom: 20px;
-                color: #333;
-            }
-            .query-area {
-                padding: 20px;
-                border-radius: 12px;
-                background: #fff;
-                box-shadow: 0 2px 8px rgba(0,0,0,0.08);
-            }
-            .footer-note {
-                text-align: center;
-                color: #888;
-                font-size: 13.5px;
-                padding: 15px 0;
-                margin-top: 20px;
-            }
-        </style>
-    
-        <div class="title-box">
-            <h1>🧠 Your Personalized AI Agent</h1>
-            <p style="margin-top: -10px;">Supercharged for Safety, Relevance, and Results</p>
-        </div>
-        """)
-        gr.HTML("""
-    <style>
-    .built-by-card {
-        margin-top: 30px;
-        padding: 15px;
-        background: #f0f4ff;
-        color: #333;
-        text-align: center;
-        border-radius: 12px;
-        font-size: 14px;
-        box-shadow: 0 2px 8px rgba(0,0,0,0.05);
-        font-weight: 500;
-        transition: all 0.3s ease;
-    }
-    .built-by-card:hover {
-        box-shadow: 0 4px 14px rgba(0,0,0,0.1);
-        background: #e6f0ff;
-    }
-    </style>
-    
-    <div class="built-by-card">
-        🚀 Built with ❤️ by <strong>Darsh Tayal</strong>
-    </div>
-    """, visible = True)
-
-    
-        # Company Description
-        comp_descri = gr.Markdown("")
-    
-        # Agent Info Features
-        gr.HTML("""
-        <div class="info-card">
-            ✅ This agent uses a <strong>relevance checker</strong> to block off-topic questions.<br>
-            🔒 It also runs a <strong>safety filter</strong> to protect users from harmful content.<br>
-            🕒 <em>Saving your time while keeping things secure.</em>
-        </div>
-        """)
-    
-        # Query Section
-        gr.HTML("<div class='query-area'>")
-        gr.Markdown("### 💬 Ask something related to your business/service:")
-        query = gr.Textbox(lines=2, placeholder="e.g., What are the top 3 features of our service?")
-        agent_submit = gr.Button("🚀 Submit Query")
-        loading_spinner = gr.HTML("""
-    <style>
-        @keyframes spin {
-            0%   { transform: rotate(0deg); }
-            100% { transform: rotate(360deg); }
-        }
-        .loader {
-            border: 5px solid #f3f3f3;
-            border-top: 5px solid #00bcd4;
-            border-radius: 50%;
-            width: 40px;
-            height: 40px;
-            animation: spin 1s linear infinite;
-        }
-        .loading-text {
-            margin-top: 8px;
-            color: #666;
-            font-size: 14px;
-            animation: pulse 1.8s infinite ease-in-out;
-        }
-        @keyframes pulse {
-            0%, 100% { opacity: 0.4; }
-            50% { opacity: 1; }
-        }
-    </style>
-    <div style="display:flex; flex-direction:column; align-items:center; margin-top: 10px;" id="spinner">
-        <div class="loader"></div>
-        <div class="loading-text">Thinking... generating magic ✨</div>
-    </div>
-""", visible=False)
-
-        answer = gr.TextArea(label='🤖 AI Response', lines=4, interactive=False)
-        gr.HTML("</div>")  # Close .query-area div
-    
-        # Footer CTA
-        gr.HTML("""
-        <div class="footer-note">
-            💡 This was just a general demo. Want a version tailored to your business?<br>
-            👉 Email me at <strong>[email protected]</strong><br>
-            📈 We can connect this agent to whatsapp, or any other marketing channel you use<br>
-            ⚙️ Start automating, or get left behind.
-        </div>
-        """)
-    setup_submit.click(fn=setter, inputs=[pdf_file, company_desc, company_name], outputs=[setup_page, processing_page, agent_page, header, comp_descri, company_generated_description, vectorstore_db])
-    agent_submit.click(fn=chat, inputs=[query, vectorstore_db, company_generated_description], outputs=[loading_spinner, answer])
-ui.launch()