Update app.py

app.py

@@ -34,9 +34,10 @@ def save_memory(memory):
         json.dump(memory, f, indent=2)
 
 # Append to memory
-def update_memory(conversation):
+def update_memory(message, response):
     memory = load_memory()
-    memory.append(conversation)
+    memory.append({"role": "user", "content": message})
+    memory.append({"role": "assistant", "content": response})
     # Optionally limit memory size
     if len(memory) > 1000:
         memory = memory[-1000:]
@@ -81,14 +82,14 @@ def train_model_on_files():
 
 # Chat response function
 def respond(message, history, system_message, max_tokens, temperature, top_p):
-    # Load or initialize model
+    # Generate response using ML model if possible
     model = load_or_initialize_model()
 
-    # Check for answers in the knowledge base
+    # Attempt to get a prediction from the ML model
     try:
         pred_label = model.predict([message])[0]
         response = f"Predicted response: {pred_label}"
-        update_memory({"user": message, "assistant": response})
+        update_memory(message, response)
         return response
     except Exception:
         pass  # Continue with GPT model if ML model doesn't have a response
@@ -96,10 +97,10 @@ def respond(message, history, system_message, max_tokens, temperature, top_p):
     # Generate response using GPT
     messages = [{"role": "system", "content": system_message}]
     for turn in history:
-        if turn["user"]:
-            messages.append({"role": "user", "content": turn["user"]})
-        if turn["assistant"]:
-            messages.append({"role": "assistant", "content": turn["assistant"]})
+        if turn["role"] == "user":
+            messages.append({"role": "user", "content": turn["content"]})
+        elif turn["role"] == "assistant":
+            messages.append({"role": "assistant", "content": turn["content"]})
     messages.append({"role": "user", "content": message})
 
     response = ""
@@ -115,11 +116,11 @@ def respond(message, history, system_message, max_tokens, temperature, top_p):
             response += token
     except Exception as e:
         response = f"Error generating response: {str(e)}"
-        update_memory({"user": message, "assistant": response})
+        update_memory(message, response)
         return response
 
     # Update memory
-    update_memory({"user": message, "assistant": response})
+    update_memory(message, response)
     return response
 
 # Gradio interface
@@ -159,7 +160,8 @@ with gr.Blocks() as demo:
 
         def handle_message(message, history, system_message, max_tokens, temperature, top_p):
             response = respond(message, history, system_message, max_tokens, temperature, top_p)
-            history.append({"user": message, "assistant": response})
+            history.append({"role": "user", "content": message})
+            history.append({"role": "assistant", "content": response})
             return history, history
 
         send_button.click(
@@ -179,7 +181,7 @@ with gr.Blocks() as demo:
             label="Upload CSV File",
             file_types=[".csv"],
             file_count="single",  # Replaced 'multiple=False' with 'file_count="single"'
-            interactive=True,
+            # Removed 'interactive=True' as it's not a valid parameter
         )
         upload_output = gr.Textbox(label="Upload Result", interactive=False)
         train_button = gr.Button("🔄 Train Model on Knowledge Base")
@@ -221,7 +223,8 @@ with gr.Blocks() as demo:
 
         def export_memory_func():
             if os.path.exists(memory_file):
-                return memory_file
+                with open(memory_file, "rb") as f:
+                    return f
             return None
 
         refresh_memory.click(display_memory, inputs=None, outputs=memory_display)
@@ -231,11 +234,12 @@ with gr.Blocks() as demo:
     with gr.Tab("💾 Download Model"):
         gr.Markdown("### Download the Trained Model")
         download_button = gr.Button("📥 Download Model")
-        model_download_output = gr.File(label="Downloadable Model", interactive=False)
+        model_download_output = gr.File(label="Downloadable Model")  # Removed 'interactive=False'
 
         def download_model():
             if os.path.exists(model_file):
-                return model_file
+                with open(model_file, "rb") as f:
+                    return f
             return None
 
         download_button.click(download_model, inputs=None, outputs=model_download_output)
@@ -247,7 +251,7 @@ with gr.Blocks() as demo:
             value="",
             label="Settings Placeholder",
             placeholder="Add settings here...",
-            interactive=False,
+            interactive=False,  # If 'interactive' is not supported, remove it
         )
 
 if __name__ == "__main__":