Update main.py

main.py

@@ -1,13 +1,12 @@
-import os  
-import shutil  
-from flask import Flask, render_template, request, jsonify  
-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  
+from flask import Flask, render_template, request, jsonify
+import os
+import shutil
+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
 from transformers import AutoTokenizer, AutoModel
 
-
 # Ensure HF_TOKEN is set  
 HF_TOKEN = os.getenv("HF_TOKEN")  
 if not HF_TOKEN:  
@@ -28,15 +27,13 @@ Settings.llm = HuggingFaceInferenceAPI(
     max_new_tokens=512,  
     generate_kwargs={"temperature": 0.1},  
 )  
-# Settings.embed_model = HuggingFaceEmbedding(  
-#     model_name="BAAI/bge-small-en-v1.5"  
-# )  
-# Replace the embedding model with XLM-R
+
+# Configure embedding model (XLM-RoBERTa model for multilingual support)
 Settings.embed_model = HuggingFaceEmbedding(
-    model_name="xlm-roberta-base"  # XLM-RoBERTa model for multilingual support
+    model_name="xlm-roberta-base"  # Multilingual support
 )
 
-# Configure tokenizer and model if required
+# Configure tokenizer and model for multilingual responses
 tokenizer = AutoTokenizer.from_pretrained("xlm-roberta-base")
 model = AutoModel.from_pretrained("xlm-roberta-base")
 
@@ -49,72 +46,80 @@ os.makedirs(PERSIST_DIR, exist_ok=True)
 chat_history = []  
 current_chat_history = []  
 
+# Data ingestion function
 def data_ingestion_from_directory():  
-    # Clear previous data by removing the persist directory  
     if os.path.exists(PERSIST_DIR):  
-        shutil.rmtree(PERSIST_DIR)  # Remove the persist directory and all its contents  
+        shutil.rmtree(PERSIST_DIR)  # Remove the persist directory and its contents  
     
-    # Recreate the persist directory after removal  
     os.makedirs(PERSIST_DIR, exist_ok=True)  
-    
-    # Load new documents from the directory  
     new_documents = SimpleDirectoryReader(PDF_DIRECTORY).load_data()  
-    
-    # Create a new index with the new documents  
     index = VectorStoreIndex.from_documents(new_documents)  
-    
-    # Persist the new index  
     index.storage_context.persist(persist_dir=PERSIST_DIR)  
 
-def handle_query(query):  
+def handle_query(query, user_language):  
     context_str = ""  
-    
+
     # Build context from current chat history  
     for past_query, response in reversed(current_chat_history):  
         if past_query.strip():  
             context_str += f"User asked: '{past_query}'\nBot answered: '{response}'\n"  
     
-    chat_text_qa_msgs = [
-        (
-            "user",
-            """You are the Taj Hotel chatbot, Taj Hotel Helper.
-    
-            *Your Role:*
+    # Define the chat response template based on selected language
+    if user_language == 'te':  # Telugu
+        response_template = """
+            మీరు తాజ్ హోటల్ చాట్‌బాట్, తాజ్ హోటల్ సహాయకుడిగా పనిచేస్తున్నారు.
+            **మీరు చేసే పాత్ర:**
+            - వినియోగదారుడి ప్రాముఖ్యమైన భాష (ఆంగ్లం, తెలుగు, హిందీ) లో సమాధానాలు ఇవ్వండి.
+            - హోటల్ యొక్క సేవలు, సదుపాయాలు మరియు విధానాలపై సమాచారం ఇవ్వండి.
+            **సూచన:**
+            - **ప్రసంగం:**  
+              {context_str}
+            - **వినియోగదారు ప్రశ్న:**  
+              {query_str}
+            **సమాధానం:** [మీ సమాధానం తెలుగులో ఇక్కడ]
+        """
+    elif user_language == 'hi':  # Hindi
+        response_template = """
+            आप ताज होटल के चैटबोट, ताज होटल हेल्पर हैं।
+            **आपकी भूमिका:**
+            - उपयोगकर्ता द्वारा चुनी गई भाषा (अंग्रेजी, हिंदी, या तेलुगु) में उत्तर दें।
+            - होटल की सेवाओं, सुविधाओं और नीतियों के बारे में जानकारी प्रदान करें।
+            **निर्देश:**
+            - **संदर्भ:**  
+              {context_str}
+            - **उपयोगकर्ता का प���रश्न:**  
+              {query_str}
+            **उत्तर:** [आपका उत्तर हिंदी में यहाँ]
+        """
+    else:  # Default to English
+        response_template = """
+            You are the Taj Hotel chatbot, Taj Hotel Helper.
+            **Your Role:**
             - Respond accurately and concisely in the user's preferred language (English, Telugu, or Hindi).
             - Provide information about the hotel’s services, amenities, and policies.
-    
-            *Instructions:*
-            - *Context:*  
+            **Instructions:**
+            - **Context:**  
               {context_str}
-            - *User's Question:*  
+            - **User's Question:**  
               {query_str}
-            
-            *Response Guidelines:*
-            1. *Language Adaptation:* Respond in the language of the question (English, Telugu, or Hindi).
-            2. *Tone:* Maintain politeness, professionalism, and the luxury branding of the Taj Hotel.
-            3. *Clarity:* Limit responses to 10-15 words for direct and clear communication.
-            4. *Knowledge Boundaries:* If unsure of an answer, respond with:
-               "I’m not sure. Please contact our staff for accurate information."
-            5. *Actionable Help:* Offer suggestions or alternative steps to guide the user where applicable.
-    
-            *Response:* [Your concise response here]
-            """.format(context_str=context_str, query_str=query)
+            **Response:** [Your concise response here]
+        """
+
+    # Create a list of chat messages with the user query and response template
+    chat_text_qa_msgs = [
+        (
+            "user",
+            response_template.format(context_str=context_str, query_str=query)
         )
     ]
 
-
-    
+    # Use the defined chat template
     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 = ""  
-    
-    # # Build context from current chat history  
-    # 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 the index and retrieve the answer
     query_engine = index.as_query_engine(text_qa_template=text_qa_template, context_str=context_str)  
     print(f"Querying: {query}")  
     answer = query_engine.query(query)  
@@ -131,16 +136,17 @@ def handle_query(query):
     current_chat_history.append((query, response))  
     return response
 
+
 app = Flask(__name__)  
 
 # Data ingestion  
 data_ingestion_from_directory()  
 
 # Generate Response  
-def generate_response(query):  
+def generate_response(query, language):  
     try:  
         # Call the handle_query function to get the response  
-        bot_response = handle_query(query)  
+        bot_response = handle_query(query, language)  
         return bot_response  
     except Exception as e:  
         return f"Error fetching the response: {str(e)}"  
@@ -155,13 +161,17 @@ def index():
 def chat():  
     try:  
         user_message = request.json.get("message")  
+        selected_language = request.json.get("language")  # Get selected language from the request
         if not user_message:  
             return jsonify({"response": "Please say something!"})  
 
-        bot_response = generate_response(user_message)  
+        if selected_language not in ['english', 'telugu', 'hindi']:  
+            return jsonify({"response": "Invalid language selected."})  
+
+        bot_response = generate_response(user_message, selected_language)  
         return jsonify({"response": bot_response})  
     except Exception as e:  
         return jsonify({"response": f"An error occurred: {str(e)}"})  
 
 if __name__ == '__main__':  
-    app.run(debug=True)
+    app.run(debug=True)