Update main.py

main.py

@@ -1,3 +1,180 @@
+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.embeddings.huggingface import HuggingFaceEmbedding
+from deep_translator import GoogleTranslator
+import google.generativeai as genai
+
+# Ensure GOOGLE_API_KEY is set
+GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
+if not GOOGLE_API_KEY:
+    raise ValueError("GOOGLE_API_KEY environment variable not set.")
+
+# Configure Gemini model
+genai.configure(api_key=GOOGLE_API_KEY)
+gemini_model = genai.GenerativeModel('gemini-flash-1.0')
+
+# Configure Llama index settings
+Settings.embed_model = HuggingFaceEmbedding(
+    model_name="sentence-transformers/paraphrase-multilingual-mpnet-base-v2"
+)
+
+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():
+    # 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
+
+    # 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):
+    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)
+
+    # Use Gemini for generating the response
+    prompt = f"""
+    Context: {context_str}
+    Question: {query}
+    """
+    gemini_response = gemini_model.generate_content(prompt)
+    response = gemini_response.text
+
+    current_chat_history.append((query, response))
+    return response
+
+app = Flask(__name__)
+
+# Data ingestion
+data_ingestion_from_directory()
+
+# Generate Response
+def generate_response(query, language):
+    try:
+        # Call the handle_query function to get the response
+        bot_response = handle_query(query)
+
+        # Map of supported languages
+        supported_languages = {
+            "hindi": "hi",
+            "bengali": "bn",
+            "telugu": "te",
+            "marathi": "mr",
+            "tamil": "ta",
+            "gujarati": "gu",
+            "kannada": "kn",
+            "malayalam": "ml",
+            "punjabi": "pa",
+            "odia": "or",
+            "urdu": "ur",
+            "assamese": "as",
+            "sanskrit": "sa",
+            "arabic": "ar",
+            "australian": "en-AU",
+            "bangla-india": "bn-IN",
+            "chinese": "zh-CN",
+            "dutch": "nl",
+            "french": "fr",
+            "filipino": "tl",
+            "greek": "el",
+            "indonesian": "id",
+            "italian": "it",
+            "japanese": "ja",
+            "korean": "ko",
+            "latin": "la",
+            "nepali": "ne",
+            "portuguese": "pt",
+            "romanian": "ro",
+            "russian": "ru",
+            "spanish": "es",
+            "swedish": "sv",
+            "thai": "th",
+            "ukrainian": "uk",
+            "turkish": "tr"
+        }
+
+        # Initialize the translated text
+        translated_text = bot_response
+
+        # Translate only if the language is supported and not English
+        try:
+            if language in supported_languages:
+                target_lang = supported_languages[language]
+                translated_text = GoogleTranslator(source='en', target=target_lang).translate(bot_response)
+                print(translated_text)
+            else:
+                print(f"Unsupported language: {language}")
+        except Exception as e:
+            # Handle translation errors
+            print(f"Translation error: {e}")
+            translated_text = "Sorry, I couldn't translate the response."
+
+        # Append to chat history
+        chat_history.append((query, translated_text))
+        return translated_text
+    except Exception as e:
+        return f"Error fetching the response: {str(e)}"
+
+# Route for the homepage
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+# Route to handle chatbot messages
+@app.route('/chat', methods=['POST'])
+def chat():
+    try:
+        user_message = request.json.get("message")
+        language = request.json.get("language")
+        if not user_message:
+            return jsonify({"response": "Please say something!"})
+
+        bot_response = generate_response(user_message, 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, host="0.0.0.0", port=int(os.environ.get("PORT", 5000)))
+
 # import os  
 # import shutil  
 # from flask import Flask, render_template, request, jsonify  
@@ -245,180 +422,3 @@
 
 # if __name__ == '__main__':  
 #     app.run(debug=True)
-
-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.embeddings.huggingface import HuggingFaceEmbedding
-from deep_translator import GoogleTranslator
-import google.generativeai as genai
-
-# Ensure GOOGLE_API_KEY is set
-GOOGLE_API_KEY = os.getenv("GOOGLE_API_KEY")
-if not GOOGLE_API_KEY:
-    raise ValueError("GOOGLE_API_KEY environment variable not set.")
-
-# Configure Gemini model
-genai.configure(api_key=GOOGLE_API_KEY)
-gemini_model = genai.GenerativeModel('gemini-flash-1.0')
-
-# Configure Llama index settings
-Settings.embed_model = HuggingFaceEmbedding(
-    model_name="sentence-transformers/paraphrase-multilingual-mpnet-base-v2"
-)
-
-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():
-    # 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
-
-    # 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):
-    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)
-
-    # Use Gemini for generating the response
-    prompt = f"""
-    Context: {context_str}
-    Question: {query}
-    """
-    gemini_response = gemini_model.generate_content(prompt)
-    response = gemini_response.text
-
-    current_chat_history.append((query, response))
-    return response
-
-app = Flask(__name__)
-
-# Data ingestion
-data_ingestion_from_directory()
-
-# Generate Response
-def generate_response(query, language):
-    try:
-        # Call the handle_query function to get the response
-        bot_response = handle_query(query)
-
-        # Map of supported languages
-        supported_languages = {
-            "hindi": "hi",
-            "bengali": "bn",
-            "telugu": "te",
-            "marathi": "mr",
-            "tamil": "ta",
-            "gujarati": "gu",
-            "kannada": "kn",
-            "malayalam": "ml",
-            "punjabi": "pa",
-            "odia": "or",
-            "urdu": "ur",
-            "assamese": "as",
-            "sanskrit": "sa",
-            "arabic": "ar",
-            "australian": "en-AU",
-            "bangla-india": "bn-IN",
-            "chinese": "zh-CN",
-            "dutch": "nl",
-            "french": "fr",
-            "filipino": "tl",
-            "greek": "el",
-            "indonesian": "id",
-            "italian": "it",
-            "japanese": "ja",
-            "korean": "ko",
-            "latin": "la",
-            "nepali": "ne",
-            "portuguese": "pt",
-            "romanian": "ro",
-            "russian": "ru",
-            "spanish": "es",
-            "swedish": "sv",
-            "thai": "th",
-            "ukrainian": "uk",
-            "turkish": "tr"
-        }
-
-        # Initialize the translated text
-        translated_text = bot_response
-
-        # Translate only if the language is supported and not English
-        try:
-            if language in supported_languages:
-                target_lang = supported_languages[language]
-                translated_text = GoogleTranslator(source='en', target=target_lang).translate(bot_response)
-                print(translated_text)
-            else:
-                print(f"Unsupported language: {language}")
-        except Exception as e:
-            # Handle translation errors
-            print(f"Translation error: {e}")
-            translated_text = "Sorry, I couldn't translate the response."
-
-        # Append to chat history
-        chat_history.append((query, translated_text))
-        return translated_text
-    except Exception as e:
-        return f"Error fetching the response: {str(e)}"
-
-# Route for the homepage
-@app.route('/')
-def index():
-    return render_template('index.html')
-
-# Route to handle chatbot messages
-@app.route('/chat', methods=['POST'])
-def chat():
-    try:
-        user_message = request.json.get("message")
-        language = request.json.get("language")
-        if not user_message:
-            return jsonify({"response": "Please say something!"})
-
-        bot_response = generate_response(user_message, 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)