trying some stuff

app.py

@@ -7,11 +7,25 @@ from huggingface_hub import hf_hub_download #load from huggingfaces
 
 
 llm = Llama(model_path= hf_hub_download(repo_id="TheBloke/Vigogne-2-7B-Instruct-GGML", filename="vigogne-2-7b-instruct.ggmlv3.q4_1.bin"), n_ctx=2048) #download model from hf/ n_ctx=2048 for high ccontext length
+chat_history = []
 
+def generate_text(message,history):
 
-def generate_text(input_text):
-    output = llm(f"Q: {input_text} \n A:", max_tokens=521, stop=["Q:", "\n"], echo=True,)
-    return output['choices'][0]['text']
+if len(history) > 0:
+        user_input, bot_response = history[-1]  # Get the latest pair from history
+        chat_history.append([user_input, message])
+    else:
+        chat_history.append([message, ""])  # If history is empty, just add the user input
+   
+input_text = message
+    output = llm(f"Q: {input_text} \n A:", max_tokens=521, stop=["Q:", "\n"], echo=True)
+    response = output['choices'][0]['text']
+
+    # Append the bot response to the chat history
+    chat_history[-1][1] = response
+
+    return response
+     
 
 input_text = gr.inputs.Textbox(lines= 10, label="Enter your input text")
 output_text = gr.outputs.Textbox(label="Output text")
@@ -24,7 +38,7 @@ examples = [
     ["What is the square root of 64?", "The square root of 64 is 8."]
 ]
 
-demo = gr.Interface(fn=generate_text, inputs=input_text, outputs=output_text, title="Llama Language Model", description=description, examples=examples)
+demo = gr.ChatInterface(random_response).launch()
 demo.queue()
 demo.launch()