Update

app.py

@@ -1,65 +1,76 @@
 import os
+import gc
 import gradio as gr
-import markdown
+import keras_nlp
 from huggingface_hub import login
+import markdown
 from bs4 import BeautifulSoup
-from functools import lru_cache
-import keras_nlp
-
-# Function to convert Markdown to HTML (preserving formatting)
-def markdown_to_html(markdown_text):
-    # Convert Markdown to HTML
-    html = markdown.markdown(markdown_text)
-    return html  # Return HTML as is (preserving the formatting)
 
 # Get the API key from environment variable
 api_key = os.getenv("HUGGINGFACE_API_KEY")
 if not api_key:
     raise ValueError("Please set the 'HUGGINGFACE_API_KEY' environment variable.")
 
-# Log in with the provided Hugging Face API token (only once)
+# Log in with the provided Hugging Face API token
 login(api_key)
 
-# Load the Keras NLP model from Hugging Face (only once)
+# Load the Keras NLP model from Hugging Face
 model_path = "MNLobago/EcoWise_model"
 gemma_lm = keras_nlp.models.GemmaCausalLM.from_preset(f"hf://{model_path}")
 
+# Function to convert Markdown to HTML (preserving the formatting)
+def markdown_to_html(markdown_text):
+    html = markdown.markdown(markdown_text)  # Convert Markdown to HTML
+    return html  # Return the HTML with formatting preserved
+
 class GemmaChat:
     def __init__(self, model, max_length=150, system=""):
         self.model = model
         self.max_length = max_length
         self.system = system
+        self.history = []
 
     def get_full_prompt(self, user_input):
         return f"User: {user_input}\nModel:"
 
     def query(self, question):
-        prompt = self.get_full_prompt(question)
+        if not self.history:
+            prompt = self.get_full_prompt(question)
+        else:
+            prompt = self.get_full_prompt(question)
+        
         response = self.model.generate(prompt, max_length=self.max_length)
         model_response = response.replace(prompt, "").strip()
-        model_response = markdown_to_html(model_response)  # Use the markdown_to_html function
+        
+        # Convert the Markdown response to HTML
+        model_response = markdown_to_html(model_response)
+        
+        # Sanitize the response if necessary (optional)
+        model_response = model_response.rstrip('?')
+
+        # Ensure the response ends with a period if it doesn't end with a punctuation mark
+        if model_response and model_response[-1] not in '.!?':
+            model_response += '.'
+
+        gc.collect()
         return model_response
 
-# Initialize the chat object (only once)
+# Initialize the chat object
 chat = GemmaChat(
     model=gemma_lm,
-    system="You are an intelligent chatbot focused on answering questions related to climate change, sustainability, and carbon footprint."
+    system="""You are an intelligent chatbot focused on answering questions related to climate change, sustainability, and carbon footprint."""
 )
 
-@lru_cache(maxsize=100)
-def cached_query(question):
-    return chat.query(question)
-
-# Gradio interface function (faster with caching)
 def chat_with_model(input_text):
-    answer = cached_query(input_text)
+    chat.history = []  # Clear history for each new query
+    answer = chat.query(input_text)
     return [("user", input_text), ("model", answer)]
 
 # Create and launch the Gradio interface
 demo = gr.Interface(
     fn=chat_with_model,
     inputs="text",
-    outputs="html",  # Update the output type to "html" to render the formatted text
+    outputs="html",  # Output as HTML to render the formatted response
     description="🌍 Welcome to EcoWise, your go-to climate-savvy chatbot! I'm here to help you."
 )