Update app.py

app.py

@@ -1,43 +1,47 @@
-import os
 import gradio as gr
-from langchain_groq import ChatGroq
-from langchain_core.prompts import ChatPromptTemplate
-
-# Get API key from environment variable
-groq_api_key = os.environ.get("GROQ_API_KEY")
-
-# Check if API key is set
-if not groq_api_key:
-    raise ValueError("❌ GROQ_API_KEY is not set. Please add it in Hugging Face Secrets.")
-
-# Initialize Groq LLM
-llm = ChatGroq(
-    groq_api_key=groq_api_key,
-    model_name="llama3-8b-8192"  # or "mixtral-8x7b-32768"
-)
-
-# Prompt template
-prompt = ChatPromptTemplate.from_template(
-    "You are Krish, a wise and witty assistant. Answer the following question:\n\n{question}"
-)
-
-# Response function
-def chat_with_krish(message):
-    try:
-        chain = prompt | llm
-        response = chain.invoke({"question": message})
-        return response.content
-    except Exception as e:
-        return f"❌ Error: {e}"
-
-# Gradio chat interface
-ui = gr.ChatInterface(
-    fn=chat_with_krish,
-    title="🦚 Meet Krish",
-    description="A wise, witty, and compassionate friend",
-    theme="soft"
-)
-
-# Launch Gradio app
+from langchain.chat_models import HuggingFaceChat
+from langchain.chains import ConversationChain
+from langchain.memory import ConversationBufferMemory
+
+MODEL_ID = "google/gemma-2b"  # Adjust if you have a local path or HF repo
+
+def load_model():
+    # Load Gemma 2B chat model on CPU
+    return HuggingFaceChat(
+        model=MODEL_ID,
+        model_kwargs={"device_map": "cpu"},
+        temperature=0.7,
+    )
+
+def main():
+    llm = load_model()
+    memory = ConversationBufferMemory(return_messages=True)
+    conversation = ConversationChain(llm=llm, memory=memory)
+
+    def respond(user_input):
+        response = conversation.run(user_input)
+        return response
+
+    title = "🦚 Meet Krish"
+    description = "A wise, witty, and compassionate friend — Chatbot named KrishWay"
+
+    with gr.Blocks() as demo:
+        gr.Markdown(f"# {title}")
+        gr.Markdown(description)
+
+        chatbot = gr.Chatbot()
+        msg = gr.Textbox(placeholder="Say something to Krish...")
+        clear = gr.Button("Clear")
+
+        def user_message(text, chat_history):
+            bot_response = respond(text)
+            chat_history = chat_history + [(text, bot_response)]
+            return "", chat_history
+
+        msg.submit(user_message, [msg, chatbot], [msg, chatbot])
+        clear.click(lambda: [], None, chatbot)
+
+    demo.launch()
+
 if __name__ == "__main__":
-    ui.launch()
+    main()