Update app.py

app.py

@@ -1,3 +1,141 @@
+# import gradio as gr
+# import torch
+# from unsloth import FastLanguageModel
+# from transformers import TextIteratorStreamer
+# import threading
+# from peft import PeftModel
+# import json
+# import time
+
+# # -----------------------------
+# # 1️⃣ Set device
+# # -----------------------------
+# device = "cuda" if torch.cuda.is_available() else "cpu"
+# print("Using device:", device)
+
+# # -----------------------------
+# # 2️⃣ Load base model (skip compilation)
+# # -----------------------------
+# base_model_name = "unsloth/gemma-3-4b-it-unsloth-bnb-4bit"
+# base_model, tokenizer = FastLanguageModel.from_pretrained(
+#     model_name=base_model_name,
+#     max_seq_length=2048,
+#     dtype=torch.float16,
+#     load_in_4bit=False,
+#     # <- avoids unsloth compilation errors
+# )
+
+# # -----------------------------
+# # 3️⃣ Load LoRA
+# # -----------------------------
+# lora_repo = "Ephraimmm/PIDGIN_gemma-3"
+# lora_model = PeftModel.from_pretrained(base_model, lora_repo, adapter_name="adapter_model")
+# FastLanguageModel.for_inference(lora_model)
+
+# # -----------------------------
+# # 4️⃣ Streaming generation function with Nigerian Pidgin system prompt
+# # -----------------------------
+# def generate_response(user_message):
+#     messages = [
+#         {
+#             "role": "system",
+#             "content": [{"type": "text", "text": "You be Nigerian assistant wey sabi Pidgin English only. No speak any other language. Reply friendly and in short sentences"}]
+#         },
+#         {
+#             "role": "user",
+#             "content": [{"type": "text", "text": user_message}]
+#         }
+#     ]
+
+#     inputs = tokenizer.apply_chat_template(
+#         messages,
+#         add_generation_prompt=True,
+#         return_tensors="pt",
+#         tokenize=True,
+#         return_dict=True
+#     ).to(device)
+
+#     streamer = TextIteratorStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+
+#     generation_kwargs = dict(
+#         **inputs,
+#         streamer=streamer,
+#         max_new_tokens=100,
+#         temperature=0.7,
+#         top_p=0.7,
+#         top_k=40,
+#         use_cache=False
+#     )
+
+#     def generate():
+#         lora_model.generate(**generation_kwargs)
+
+#     thread = threading.Thread(target=generate)
+#     thread.start()
+
+#     full_response = ""
+#     for new_token in streamer:
+#         if new_token:
+#             full_response += new_token
+#     thread.join()
+#     return full_response
+
+# # -----------------------------
+# # 5️⃣ Chat + Save
+# # -----------------------------
+# chat_history = []
+
+# def chat(user_message):
+#     bot_response = generate_response(user_message)
+#     chat_history.append((user_message, bot_response))
+#     return chat_history, ""  # also clears input box
+
+# def save_conversation():
+#     if not chat_history:
+#         return None
+    
+#     conversation = []
+#     for user_msg, bot_msg in chat_history:
+#         conversation.append({"role": "user", "content": str(user_msg)})
+#         conversation.append({"role": "assistant", "content": str(bot_msg)})
+    
+#     timestamp = time.strftime("%Y%m%d-%H%M%S")
+#     file_path = f"conversation_{timestamp}.txt"   # save as TXT not JSON
+    
+#     with open(file_path, "w", encoding="utf-8") as f:
+#         json.dump(conversation, f, indent=4, ensure_ascii=False)
+    
+#     return file_path
+
+# # -----------------------------
+# # 6️⃣ Gradio interface
+# # -----------------------------
+# with gr.Blocks() as demo:
+#     gr.Markdown("# Nigerian PIDGIN Assistant")
+#     gr.Markdown("Chat with a Nigerian assistant that only speaks Pidgin English.")
+
+#     chatbot = gr.Chatbot(label="Conversation")
+#     user_input = gr.Textbox(label="Your message", placeholder="Type your message here...")
+    
+#     with gr.Row():
+#         send_button = gr.Button("Send")
+#         save_button = gr.Button("Save Conversation")
+#         download_file = gr.File(label="Download Conversation")
+
+#     send_button.click(chat, inputs=user_input, outputs=[chatbot, user_input])
+#     save_button.click(save_conversation, outputs=download_file)
+
+# demo.launch()
+# # iface = gr.Interface(
+# #     fn=generate_response,
+# #     inputs=gr.Textbox(lines=2, placeholder="Enter your message..."),
+# #     outputs=gr.Textbox(label="PIDGIN Assistant"),
+# #     title="Nigerian PIDGIN Assistant",
+# #     description="Chat with a Nigerian assistant that only speaks Pidgin English."
+# # )
+
+# # iface.launch()
+
 import gradio as gr
 import torch
 from unsloth import FastLanguageModel
@@ -6,6 +144,7 @@ import threading
 from peft import PeftModel
 import json
 import time
+import os
 
 # -----------------------------
 # 1️⃣ Set device
@@ -22,7 +161,6 @@ base_model, tokenizer = FastLanguageModel.from_pretrained(
     max_seq_length=2048,
     dtype=torch.float16,
     load_in_4bit=False,
-    # <- avoids unsloth compilation errors
 )
 
 # -----------------------------
@@ -33,7 +171,7 @@ lora_model = PeftModel.from_pretrained(base_model, lora_repo, adapter_name="adap
 FastLanguageModel.for_inference(lora_model)
 
 # -----------------------------
-# 4️⃣ Streaming generation function with Nigerian Pidgin system prompt
+# 4️⃣ Streaming generation function
 # -----------------------------
 def generate_response(user_message):
     messages = [
@@ -92,7 +230,11 @@ def chat(user_message):
 
 def save_conversation():
     if not chat_history:
-        return None
+        # Return a small empty txt file instead of None (to avoid Gradio error)
+        file_path = "conversation_empty.txt"
+        with open(file_path, "w", encoding="utf-8") as f:
+            f.write("[]")
+        return file_path
     
     conversation = []
     for user_msg, bot_msg in chat_history:
@@ -126,12 +268,4 @@ with gr.Blocks() as demo:
     save_button.click(save_conversation, outputs=download_file)
 
 demo.launch()
-# iface = gr.Interface(
-#     fn=generate_response,
-#     inputs=gr.Textbox(lines=2, placeholder="Enter your message..."),
-#     outputs=gr.Textbox(label="PIDGIN Assistant"),
-#     title="Nigerian PIDGIN Assistant",
-#     description="Chat with a Nigerian assistant that only speaks Pidgin English."
-# )
 
-# iface.launch()