ew version

app.py

@@ -16,7 +16,7 @@ import pandas as pd
 import json
 
 # Enable logging for debugging
-logging.basicConfig(level=logging.INFO)  # Changed to INFO to reduce verbosity
+logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
 # Function to clean the API key
@@ -100,7 +100,7 @@ def is_valid_input(text):
 # Initialize the LLM using ChatGroq with GROQ's API
 def initialize_llm(model, temperature, max_tokens):
     try:
-        # Allocate a portion of tokens for the prompt, e.g., 20%
+        # Allocate a portion of tokens for the prompt
         prompt_allocation = int(max_tokens * 0.2)
         response_max_tokens = max_tokens - prompt_allocation
         if response_max_tokens <= 50:
@@ -109,8 +109,8 @@ def initialize_llm(model, temperature, max_tokens):
         llm = ChatGroq(
             model=model,
             temperature=temperature,
-            max_tokens=response_max_tokens,  # Adjusted max_tokens
-            api_key=api_key  # Ensure the API key is passed correctly
+            max_tokens=response_max_tokens,
+            api_key=api_key
         )
         logger.info("LLM initialized successfully.")
         return llm
@@ -133,11 +133,11 @@ def create_rag_pipeline(file_paths, model, temperature, max_tokens):
         # Initialize the embedding model
         embedding_model = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")
 
-        # Use a temporary directory for Chroma vectorstore to prevent caching issues on Hugging Face Spaces
+        # Use a temporary directory for Chroma vectorstore
         vectorstore = Chroma.from_documents(
             documents=splits,
             embedding=embedding_model,
-            persist_directory="/tmp/chroma_db"  # Temporary storage directory
+            persist_directory="/tmp/chroma_db"
         )
         vectorstore.persist()  # Save the database to disk
         logger.info("Vectorstore initialized and persisted successfully.")
@@ -148,13 +148,10 @@ def create_rag_pipeline(file_paths, model, temperature, max_tokens):
             input_variables=["context", "question"],
             template="""
             You are an AI assistant with expertise in daily wellness. Your aim is to provide detailed and comprehensive solutions regarding daily wellness topics without unnecessary verbosity.
-
             Context:
             {context}
-
             Question:
             {question}
-
             Provide a thorough and complete answer, including relevant examples and a suggested schedule. Ensure that the response does not end abruptly.
             """
         )
@@ -172,109 +169,41 @@ def create_rag_pipeline(file_paths, model, temperature, max_tokens):
         logger.debug("Exception details:", exc_info=True)
         return None, f"Error creating RAG pipeline: {e}"
 
-# Define positive and negative words for rule-based sentiment analysis
-POSITIVE_WORDS = {
-    "good", "great", "excellent", "amazing", "wonderful", "fantastic", "positive",
-    "helpful", "satisfied", "happy", "love", "liked", "enjoyed", "beneficial",
-    "superb", "awesome", "nice", "brilliant", "favorable", "pleased"
-}
-
-NEGATIVE_WORDS = {
-    "bad", "terrible", "awful", "poor", "disappointed", "unsatisfied", "hate",
-    "hated", "dislike", "dislikes", "worst", "negative", "not helpful", "frustrated",
-    "unhappy", "dissatisfied", "unfortunate", "horrible", "annoyed", "problem", "issues"
-}
-
-# Function to handle feedback with rule-based sentiment analysis
-def handle_feedback(feedback_text):
-    """
-    Handles user feedback by analyzing its sentiment and providing a dynamic response.
-    Stores the feedback in a temporary file for persistence during the session.
-
-    Parameters:
-    - feedback_text (str): The feedback provided by the user.
-
-    Returns:
-    - str: Acknowledgment message based on feedback sentiment.
-    """
-    if feedback_text and feedback_text.strip() != "":
-        # Normalize feedback text to lowercase for comparison
-        feedback_lower = feedback_text.lower()
-
-        # Count positive and negative words
-        positive_count = sum(word in feedback_lower for word in POSITIVE_WORDS)
-        negative_count = sum(word in feedback_lower for word in NEGATIVE_WORDS)
-
-        # Determine sentiment based on counts
-        if positive_count > negative_count:
-            sentiment = "positive"
-            acknowledgment = "Thank you for your positive feedback! We're glad to hear that you found our service helpful."
-        elif negative_count > positive_count:
-            sentiment = "negative"
-            acknowledgment = "We're sorry to hear that you're not satisfied. Your feedback is valuable to us, and we'll strive to improve."
-        else:
-            sentiment = "neutral"
-            acknowledgment = "Thank you for your feedback. We appreciate your input."
-
-        # Log the feedback with sentiment
-        logger.info(f"User Feedback: {feedback_text} | Sentiment: {sentiment}")
-
-        # Optionally, store feedback in a temporary file
-        try:
-            with open("/tmp/user_feedback.txt", "a") as f:
-                f.write(f"{feedback_text} | Sentiment: {sentiment}\n")
-            logger.debug("Feedback stored successfully in /tmp/user_feedback.txt.")
-        except Exception as e:
-            logger.error(f"Error storing feedback: {e}")
-
-        return acknowledgment
-    else:
-        return "No feedback provided."
-
 # Initialize the RAG pipeline once at startup
-# Define the file paths (ensure 'AIChatbot.csv' is in the root directory of your Space)
 file_paths = ['AIChatbot.csv']
-model = "llama3-8b-8192"  # Default model name
-temperature = 0.7  # Default temperature
-max_tokens = 500  # Default max tokens
+model = "llama3-8b-8192"
+temperature = 0.7
+max_tokens = 500
 
 rag_chain, message = create_rag_pipeline(file_paths, model, temperature, max_tokens)
 if rag_chain is None:
     logger.error("Failed to initialize RAG pipeline at startup.")
-    # Depending on your preference, you might want to exit or continue. Here, we'll continue.
 
 # Function to answer questions with input validation and post-processing
-def answer_question(model, temperature, max_tokens, question, feedback):
+def answer_question(model, temperature, max_tokens, question):
     # Validate input
     if not is_valid_input(question):
         logger.info("Received invalid input from user.")
-        return "Please provide a valid question or input containing meaningful text.", ""
+        return "Please provide a valid question or input containing meaningful text."
 
-    # Check if the RAG pipeline is initialized
     if rag_chain is None:
         logger.error("RAG pipeline is not initialized.")
-        return "The system is currently unavailable. Please try again later.", ""
+        return "The system is currently unavailable. Please try again later."
 
     try:
         answer = rag_chain.run(question)
         logger.info("Question answered successfully.")
         # Post-process to ensure the answer ends with complete sentences
         complete_answer = ensure_complete_sentences(answer)
-
-        # Handle feedback
-        feedback_response = handle_feedback(feedback)
-
-        return complete_answer, feedback_response
+        return complete_answer
     except Exception as e_inner:
         logger.error(f"Error during RAG pipeline execution: {e_inner}")
         logger.debug("Exception details:", exc_info=True)
-        return f"Error during RAG pipeline execution: {e_inner}", ""
+        return f"Error during RAG pipeline execution: {e_inner}"
 
-# Gradio Interface with Feedback Mechanism
-def gradio_interface(model, temperature, max_tokens, question, feedback):
-    # Optionally, you can add functionality to update the RAG pipeline if model or parameters change
-    # For now, we'll ignore changes to model parameters after initialization
-    return answer_question(model, temperature, max_tokens, question, feedback)
+# Gradio Interface (no feedback)
+def gradio_interface(model, temperature, max_tokens, question):
+    return answer_question(model, temperature, max_tokens, question)
 
 # Define Gradio UI
 interface = gr.Interface(
@@ -291,7 +220,7 @@ interface = gr.Interface(
             maximum=1,
             step=0.01,
             value=temperature,
-            info="Controls the randomness of the response. Higher values like 0.8 will make the output more random, while lower values like 0.2 will make it more focused and deterministic."
+            info="Controls the randomness of the response. Higher values make output more random."
         ),
         gr.Slider(
             label="Max Tokens",
@@ -299,31 +228,22 @@ interface = gr.Interface(
             maximum=2048,
             step=1,
             value=max_tokens,
-            info="Determines the maximum number of tokens in the response. Higher values allow for longer answers."
+            info="Determines the maximum number of tokens in the response."
         ),
         gr.Textbox(
             label="Question",
             placeholder="e.g., What is box breathing and how does it help reduce anxiety?"
-        ),
-        gr.Textbox(
-            label="Feedback",
-            placeholder="Provide your feedback here...",
-            lines=2
         )
     ],
-    outputs=[
-        "text",
-        "text"
-    ],
+    outputs="text",
     title="Daily Wellness AI",
     description="Ask questions about daily wellness and get detailed solutions.",
     examples=[
-        ["llama3-8b-8192", 0.7, 500, "What is box breathing and how does it help reduce anxiety?", "Great explanation!"],
-        ["llama3-8b-8192", 0.6, 600, "Provide a daily wellness schedule incorporating box breathing techniques.", "Very helpful, thank you!"]
+        ["llama3-8b-8192", 0.7, 500, "What is box breathing and how does it help reduce anxiety?"],
+        ["llama3-8b-8192", 0.6, 600, "Provide a daily wellness schedule incorporating box breathing techniques."]
     ],
-    allow_flagging="never"  # Disable default flagging; using custom feedback
+    allow_flagging="never"
 )
 
-# Launch Gradio app without share=True (not supported on Hugging Face Spaces)
 if __name__ == "__main__":
     interface.launch(server_name="0.0.0.0", server_port=7860, debug=True)