Update main.py

main.py

@@ -1,5 +1,78 @@
 from flask import Flask, render_template, request, jsonify
 from gradio_client import Client
+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
+from huggingface_hub import InferenceClient
+
+repo_id = "meta-llama/Meta-Llama-3-8B-Instruct"
+llm_client = InferenceClient(
+    model=repo_id,
+    token=os.getenv("HF_TOKEN"),
+)
+
+os.environ["HF_TOKEN"] = os.getenv("HF_TOKEN")
+# Configure 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"
+)
+
+PERSIST_DIR = "db"
+PDF_DIRECTORY = 'data'
+
+# Ensure directories exist
+os.makedirs(PDF_DIRECTORY, exist_ok=True)
+os.makedirs(PERSIST_DIR, exist_ok=True)
+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 the Hotel voice chatbot and your name is hotel helper. Your goal is to provide accurate, professional, and helpful answers to user queries based on the hotel's data. Always ensure your responses are clear and concise. Give response within 10-15 words only. You need to give an answer in the same language used by the user.       
+            {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)
+    print(query)
+    answer = query_engine.query(query)
+
+    if hasattr(answer, 'response'):
+        response = answer.response
+    elif isinstance(answer, dict) and 'response' in answer:
+        response = answer['response']
+    else:
+        response = "Sorry, I couldn't find an answer."
+    current_chat_history.append((query, response))
+    return response
 
 app = Flask(__name__)
 
@@ -10,16 +83,25 @@ except Exception as e:
     print(f"Error initializing Gradio client: {str(e)}")
     client = None
 
-# Function to fetch the response from Gradio model
+# # Function to fetch the response from Gradio model
+# def generate_response(query):
+#     if client is None:
+#         return "Model is unavailable at the moment. Please try again later."
+#     try:
+#         result = client.predict(query=query, api_name="/predict")
+#         return result
+#     except Exception as e:
+#         return f"Error fetching the response: {str(e)}"
+# Generate Response
 def generate_response(query):
-    if client is None:
-        return "Model is unavailable at the moment. Please try again later."
     try:
-        result = client.predict(query=query, api_name="/predict")
-        return result
+        # Call the handle_query function to get the response
+        bot_response = handle_query(query)
+        return bot_response
     except Exception as e:
         return f"Error fetching the response: {str(e)}"
 
+
 # Route for the homepage
 @app.route('/')
 def index():