try

app.py

@@ -1,32 +1,52 @@
-import gradio as gr
-from transformers import pipeline
+# import gradio as gr
+# from transformers import pipeline
 
-model = pipeline("text-generation", model="umarbutler/open-australian-legal-llm")
+# model = pipeline("text-generation", model="umarbutler/open-australian-legal-llm")
     
-def chat_fn(message, history):
-    prompt = "You are a helpful, friendly and very senior lawyer who is an expert in criminal law advocacy and procedure. You advise clients on how to deal with police, police powers and what to do if arrested. You also represent in court and know everything about evidence and procedure. Here's your client's message: " + message
+# def chat_fn(message, history):
+#     prompt = "You are a helpful, friendly and very senior lawyer who is an expert in criminal law advocacy and procedure. You advise clients on how to deal with police, police powers and what to do if arrested. You also represent in court and know everything about evidence and procedure. Here's your client's message: " + message
     
-    # Generate text using the model
-    response = model(prompt, max_length=500, num_return_sequences=1)
+#     # Generate text using the model
+#     response = model(prompt, max_length=500, num_return_sequences=1)
     
-    # Extract the generated text from the response
-    reply = response[0]['generated_text']
+#     # Extract the generated text from the response
+#     reply = response[0]['generated_text']
     
-    # Update history with the new message format
-    history.append({"role": "user", "content": message})
-    history.append({"role": "assistant", "content": reply})
-    return history
+#     # Update history with the new message format
+#     history.append({"role": "user", "content": message})
+#     history.append({"role": "assistant", "content": reply})
+#     return history
 
-with gr.Blocks() as dennis_denuto:
-    chatbot = gr.Chatbot(
-        placeholder="<strong>It's the vibe.</strong><br>Ask anything about criminal law and procedure in Australia.",
-        type="messages"
-    )
-    gr.ChatInterface(
-        fn=chat_fn, 
-        type="messages", 
-        chatbot=chatbot, 
-        save_history=True
-    )
+# with gr.Blocks() as dennis_denuto:
+#     chatbot = gr.Chatbot(
+#         placeholder="<strong>It's the vibe.</strong><br>Ask anything about criminal law and procedure in Australia.",
+#         type="messages"
+#     )
+#     gr.ChatInterface(
+#         fn=chat_fn, 
+#         type="messages", 
+#         chatbot=chatbot, 
+#         save_history=True
+#     )
     
-dennis_denuto.launch()
+# dennis_denuto.launch()
+
+import gradio as gr
+from transformers import pipeline
+
+model = pipeline("text-generation", model="umarbutler/open-australian-legal-llm")
+
+def chatbot(input_text):
+    prompt = "You are a helpful, friendly and very senior lawyer who is an expert in criminal law advocacy and procedure. You advise clients on how to deal with police, police powers and what to do if arrested. You also represent in court and know everything about evidence and procedure. Here's your client's message: " + input_text
+    response = model(prompt, max_length=100, num_return_sequences=1)
+    return response[0]['generated_text']
+
+iface = gr.Interface(
+    fn=chatbot,
+    inputs=gr.Textbox(lines=2, placeholder="Enter your question here..."),
+    outputs="text",
+    title="Dennis Denuto LLM",
+    description="Ask anything about criminal law and procedure in Australia. It's the vibe."
+)
+
+iface.launch()

requirements.txt

@@ -1,4 +1,4 @@
+huggingface_hub==0.22.2
 gradio
-huggingface_hub
 transformers
-torch
+torch  # or tensorflow, depending on your model