Update app.py

app.py

@@ -1,169 +1,55 @@
-from dotenv import load_dotenv
 import gradio as gr
-import os
-from llama_index.core import StorageContext, load_index_from_storage, VectorStoreIndex, SimpleDirectoryReader, ChatPromptTemplate, Settings
-from llama_index.llms.huggingface import HuggingFaceInferenceAPI
-from llama_index.embeddings.huggingface import HuggingFaceEmbedding
-import datetime
-import uuid
-import random
-
-def select_random_name():
-    names = ['Clara', 'Lily']
-    return random.choice(names)
-
-# Example usage
-# Load environment variables
-load_dotenv()
-
-# Configure the Llama index settings
-Settings.llm = HuggingFaceInferenceAPI(
-    model_name="meta-llama/Meta-Llama-3-8B-Instruct",
-    tokenizer_name="meta-llama/Meta-Llama-3-8B-Instruct",
-    context_window=3000,
-    token=os.getenv("HF_TOKEN"),
-    max_new_tokens=512,
-    generate_kwargs={"temperature": 0.1},
-)
-Settings.embed_model = HuggingFaceEmbedding(
-    model_name="BAAI/bge-small-en-v1.5"
-)
-
-# Define the directory for persistent storage and data
-PERSIST_DIR = "db"
-PDF_DIRECTORY = 'data'  # Changed to the directory containing PDFs
-
-# Ensure directories exist
-os.makedirs(PDF_DIRECTORY, exist_ok=True)
-os.makedirs(PERSIST_DIR, exist_ok=True)
-
-# Variable to store current chat conversation
-current_chat_history = []
-
-def data_ingestion_from_directory():
-    # Use SimpleDirectoryReader on the directory containing the PDF files
-    documents = SimpleDirectoryReader(PDF_DIRECTORY).load_data()
-    storage_context = StorageContext.from_defaults()
-    index = VectorStoreIndex.from_documents(documents)
-    index.storage_context.persist(persist_dir=PERSIST_DIR)
-
-def handle_query(query):
-    chat_text_qa_msgs = [
-        (
-            "user",
-            """
-            You are the Clara Redfernstech chatbot. Your goal is to provide accurate, professional, and helpful answers to user queries based on the company's data. Always ensure your responses are clear and concise. give response within 10-15 words only       
-            {context_str}
-            Question:
-            {query_str}
-            """
-        )
-    ]
-    text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
-
-    # Load index from storage
-    storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
-    index = load_index_from_storage(storage_context)
-
-    # Use chat history to enhance response
-    context_str = ""
-    for past_query, response in reversed(current_chat_history):
-        if past_query.strip():
-            context_str += f"User asked: '{past_query}'\nBot answered: '{response}'\n"
-
-    query_engine = index.as_query_engine(text_qa_template=text_qa_template, context_str=context_str)
-    answer = query_engine.query(query)
-
-    if hasattr(answer, 'response'):
-        response = answer.response
-    elif isinstance(answer, dict) and 'response' in answer:
-        response = answer['response']
+import firebase_admin
+from firebase_admin import db, credentials
+from gradio_client import Client
+
+# Initialize Firebase
+cred = credentials.Certificate("redfernstech-fd8fe-firebase-adminsdk-g9vcn-0537b4efd6.json")
+firebase_admin.initialize_app(cred, {"databaseURL": "https://redfernstech-fd8fe-default-rtdb.firebaseio.com/"})
+
+# Initialize Gradio Client
+client = Client("vilarin/Llama-3.1-8B-Instruct")
+
+def process_inputs(ip_address, chat_history):
+    # Process chat history with the model
+    result = client.predict(
+        message=chat_history,
+        system_prompt="summarize the text and give me the client interest within 30-40 words only in bullet points",
+        temperature=0.8,
+        max_new_tokens=1024,
+        top_p=1,
+        top_k=20,
+        penalty=1.2,
+        api_name="/chat"
+    )
+    
+    # Print result for debugging
+    print(result)
+    
+    # Check Firebase for existing IP address
+    ref = db.reference('ip_addresses')
+    ip_data = ref.get()
+    
+    if ip_address in ip_data:
+        # Update existing record
+        ref.child(ip_address).update({"chat_history": chat_history, "result": result})
     else:
-        response = "Sorry, I couldn't find an answer."
-
-    # Update current chat history
-    current_chat_history.append((query, response))
-
-    return response
-
-# Example usage: Process PDF ingestion from directory
-print("Processing PDF ingestion from directory:", PDF_DIRECTORY)
-data_ingestion_from_directory()
-
-def predict(message, history):
-    logo_html = '''
-    <div class="circle-logo">
-      <img src="https://rb.gy/8r06eg" alt="FernAi">
-    </div>
-    '''
-    response = handle_query(message)
-    response_with_logo = f'<div class="response-with-logo">{logo_html}<div class="response-text">{response}</div></div>'
-    return response_with_logo
-
-def chat_interface(message, history):
-    try:
-        # Process the user message and generate a response
-        response = handle_query(message)
-
-        # Update chat history
-        current_chat_history.append((message, response))
-
-        return response
-    except Exception as e:
-        return str(e)
-
-# Custom CSS for styling
-css = '''
-  .circle-logo {
-  display: inline-block;
-  width: 40px;
-  height: 40px;
-  border-radius: 50%;
-  overflow: hidden;
-  margin-right: 10px;
-  vertical-align: middle;
-}
-.circle-logo img {
-  width: 100%;
-  height: 100%;
-  object-fit: cover;
-}
-.response-with-logo {
-  display: flex;
-  align-items: center;
-  margin-bottom: 10px;
-}
-footer {
-    display: none !important;
-    background-color: #F8D7DA;
-  }
-.svelte-1ed2p3z p {
-  font-size: 24px;
-  font-weight: bold;
-  line-height: 1.2;
-  color: #111;
-  margin: 20px 0;
-}
-label.svelte-1b6s6s {display: none}
-div.svelte-rk35yg {display: none;}
-div.progress-text.svelte-z7cif2.meta-text {display: none;}
-'''
-
-# Define JavaScript for redirection
-js = '''
-<script>
-function redirectToPage() {
-  window.location.href = "https://example.com";  // Replace with your target URL
-}
-</script>
-<button onclick="redirectToPage()">Redirect to another page</button>
-'''
+        # Create new record
+        ref.child(ip_address).set({"chat_history": chat_history, "result": result})
+    
+    return result
+
+# Define the Gradio interface
+interface = gr.Interface(
+    fn=process_inputs,
+    inputs=[
+        gr.Textbox(label="IP Address"),
+        gr.Textbox(label="Chat History")
+    ],
+    outputs="text",
+    title="Chat History Processor",
+    description="Enter an IP address and chat history to process and save to Firebase."
+)
 
-gr.ChatInterface(
-    fn=chat_interface,
-    inputs="text",
-    outputs="html",
-    live=True,
-    css=css,
-    description=js
-).launch()
+# Launch the Gradio interface
+interface.launch()