Update app.py

app.py

@@ -1,35 +1,74 @@
 import os
+from groq import Groq
 import gradio as gr
-from langchain.chat_models import ChatOpenAI
-from langchain import LLMChain, PromptTemplate
-from langchain.memory import ConversationBufferMemory
+import logging
 
-OPENAI_API_KEY="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"
-OPENAI_API_KEY=os.getenv('OPENAI_API_KEY')
+# Set up logging
+logging.basicConfig(level=logging.DEBUG)
+logger = logging.getLogger(__name__)
 
-template = """You are a helpful assistant to answer all user queries.
-{chat_history}
-User: {user_message}
-Chatbot:"""
+# Initialize the Groq client
+api_key = os.environ.get("GROQ_API_KEY")
+if not api_key:
+    logger.error("GROQ_API_KEY environment variable is not set.")
+    raise ValueError("GROQ_API_KEY environment variable is required.")
+client = Groq(api_key=api_key)
 
-prompt = PromptTemplate(
-    input_variables=["chat_history", "user_message"], template=template
-)
+MODEL_NAME = os.environ.get("MODEL_NAME", "llama3-8b-8192")
 
-memory = ConversationBufferMemory(memory_key="chat_history")
+# Define a function to handle chat completions
+def get_completion(user_input):
+    try:
+        completion = client.chat.completions.create(
+            model=MODEL_NAME,
+            messages=[
+                {"role": "system", "content": "You are a helpful assistant."},
+                {"role": "user", "content": user_input}
+            ],
+            temperature=1,
+            max_tokens=1024,
+            top_p=1,
+            stream=True,
+            stop=None,
+        )
+        
+        response = ""
+        for chunk in completion:
+            response += chunk.choices[0].delta.content or ""
+        
+        return response
+    except Exception as e:
+        logger.error(f"Error during completion: {e}")
+        return "Sorry, I encountered an error while processing your request."
 
-llm_chain = LLMChain(
-    llm=ChatOpenAI(temperature='0.5', model_name="gpt-3.5-turbo"),
-    prompt=prompt,
-    verbose=True,
-    memory=memory,
-)
-
-def get_text_response(user_message,history):
-    response = llm_chain.predict(user_message = user_message)
-    return response
-
-demo = gr.ChatInterface(get_text_response)
+# Launch Gradio interface
+def launch_interface():
+    demo = gr.Interface(
+        fn=get_completion,
+        inputs=gr.Textbox(
+            label="Enter your query:",
+            placeholder="Ask me anything...",
+            lines=2,
+            max_lines=5,
+            show_label=True,
+            interactive=True
+        ),
+        outputs=gr.Textbox(
+            label="Response:",
+            interactive=False,
+            show_label=True,
+            lines=6,
+            max_lines=10
+        ),
+        title="Mr AI",
+        description="Ask anything and get a helpful response.",
+        theme="default",
+        css=".gr-box { border-radius: 10px; border: 1px solid #ccc; padding: 10px; }",
+        allow_flagging="never"
+    )
+    
+    logger.info("Starting Gradio interface")
+    demo.launch(share=True)
 
 if __name__ == "__main__":
-    demo.launch() 
+    launch_interface()