Update app.py

app.py

@@ -1,55 +1,159 @@
+from dotenv import load_dotenv
+from fastapi import FastAPI, Request
 import gradio as gr
+import os
 import firebase_admin
 from firebase_admin import db, credentials
-from gradio_client import Client
+import datetime
+import uuid
+import random
+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 threading
+import uvicorn
 
-# Initialize Firebase
-cred = credentials.Certificate("redfernstech-fd8fe-firebase-adminsdk-g9vcn-0537b4efd6.json")
+# Load environment variables
+load_dotenv()
+
+# Authenticate to Firebase
+cred = credentials.Certificate("/content/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:
-        # 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."
+app = FastAPI()
+
+# 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"
 )
 
-# Launch the Gradio interface
-interface.launch()
+# Define directories
+PERSIST_DIR = "db"
+PDF_DIRECTORY = 'data'
+os.makedirs(PDF_DIRECTORY, exist_ok=True)
+os.makedirs(PERSIST_DIR, exist_ok=True)
+
+# Variable to store chat history
+current_chat_history = []
+
+def data_ingestion_from_directory():
+    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 Clara, the Redfernstech chatbot. Provide accurate, professional, and concise answers based on the data. Respond within 10-15 words only.
+            {context_str}
+            Question:
+            {query_str}
+            """
+        )
+    ]
+    text_qa_template = ChatPromptTemplate.from_messages(chat_text_qa_msgs)
+
+    storage_context = StorageContext.from_defaults(persist_dir=PERSIST_DIR)
+    index = load_index_from_storage(storage_context)
+
+    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)
+
+    response = answer.response if hasattr(answer, 'response') else "Sorry, I couldn't find an answer."
+
+    current_chat_history.append((query, response))
+
+    return response
+
+def save_chat_message(username, email, session_id, message_data):
+    ref = db.reference(f'/chat_history/{username}/{email}/{session_id}')
+    ref.push().set(message_data)
+
+def chat_interface(message, history, request: gr.Request):
+    try:
+        username = request.query_params.get('username')
+        email = request.query_params.get('email')
+        session_id = str(uuid.uuid4())
+        response = handle_query(message)
+
+        message_data = {
+            "sender": request.client.host,
+            "message": message,
+            "response": response,
+            "timestamp": datetime.datetime.now().isoformat()
+        }
+
+        save_chat_message(username, email, session_id, message_data)
+
+        return response
+    except Exception as e:
+        return str(e)
+
+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;}
+'''
+
+@app.get("/chat")
+async def chat_ui(username: str, email: str):
+    with gr.Blocks(css=css) as demo:
+        gr.ChatInterface(
+            fn=chat_interface,
+            css=css,
+            description="Clara",
+            clear_btn=None,
+            undo_btn=None,
+            retry_btn=None
+        ).launch()
+    return {"message": "Chat interface launched."}
+
+if __name__ == "__main__":
+    data_ingestion_from_directory()
+    threading.Thread(target=lambda: uvicorn.run(app, host="0.0.0.0", port=8000), daemon=True).start()