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README.md

@@ -1,12 +1,6 @@
 ---
-title: Echo Chatbot
-emoji: 🏢
-colorFrom: red
-colorTo: yellow
-sdk: gradio
-sdk_version: 4.28.2
+title: echo-chatbot
 app_file: app.py
-pinned: false
+sdk: gradio
+sdk_version: 4.27.0
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,18 @@
+import ollama
+import gradio as gr
+def chat(question, history):
+  history_format = []
+  for human, assistant in history:
+      history_format.append({"role": "user", "content": human})
+      history_format.append({"role": "assistant", "content":assistant})
+  history_format.append({'role': 'user', 'content': question})
+
+  messages=history_format
+  stream = ollama.chat(model='llama3', messages=messages)
+  #print(history_format)
+  return stream['message']['content']
+
+# Gradio interface
+assistant_icon = gr.Image(value="C:/Users/chris/Downloads/japanese-lama-ghibli-artstyle.jpeg", width=32, height=32)  # adjust the size as needed
+assistant_img = gr.Image(value="C:/Users/chris/Downloads/japanese-lama-ghibli-artstyle.jpeg", elem_id="assistant_img")
+gr.ChatInterface(fn=chat, title="Chat Bot",chatbot=gr.Chatbot(height=300)).launch()

chatbot.ipynb

@@ -0,0 +1,150 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import gradio as gr\n",
+    "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\n",
+    "from langchain.chains import LLMChain\n",
+    "from langchain_community.llms import GPT4All\n",
+    "from langchain_core.prompts import PromptTemplate\n",
+    "from langchain.memory import ConversationBufferMemory\n",
+    "from langchain_core.prompts import (\n",
+    "    ChatPromptTemplate,\n",
+    "    MessagesPlaceholder,\n",
+    "    SystemMessagePromptTemplate,\n",
+    "    HumanMessagePromptTemplate,\n",
+    ")\n",
+    "\n",
+    "def response(input, history):\n",
+    "    # Path to your local GPT4All model\n",
+    "    local_path = \"C:/Users/chris/AppData/Local/nomic.ai/GPT4All/Meta-Llama-3-8B-Instruct.Q4_0.gguf\"\n",
+    "\n",
+    "    # Callbacks support token-wise streaming\n",
+    "    callbacks = [StreamingStdOutCallbackHandler()]\n",
+    "\n",
+    "    # Initialize GPT4All model\n",
+    "    llm = GPT4All(streaming= True, model=local_path, backend=\"gptj\", callbacks=callbacks, verbose=False)\n",
+    "\n",
+    "    prompt = ChatPromptTemplate(\n",
+    "        messages=[\n",
+    "            SystemMessagePromptTemplate.from_template(\n",
+    "                \"You are a cute anime chatbot having a conversation with a human.\"\n",
+    "            ),\n",
+    "            # The `variable_name` here is what must align with memory\n",
+    "            MessagesPlaceholder(variable_name=\"{history}\"),\n",
+    "            HumanMessagePromptTemplate.from_template(\"{question}\")\n",
+    "        ]\n",
+    "    )\n",
+    "\n",
+    "    # Initialize conversation memory\n",
+    "    memory = ConversationBufferMemory(memory_key=\"{history}\", return_messages=True)\n",
+    "    #memory = history\n",
+    "\n",
+    "    # Create an LLMChain instance for the conversation\n",
+    "    conversation = LLMChain(\n",
+    "        llm=llm,\n",
+    "        prompt=prompt,\n",
+    "        verbose=False,\n",
+    "        memory=memory\n",
+    "    )\n",
+    "\n",
+    "    # Generate a response using the input and the LLMChain\n",
+    "    response = conversation.invoke(input={\"question\": input})[\"text\"]\n",
+    "\n",
+    "    return response\n",
+    "\n",
+    "gr.ChatInterface(response).launch()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Local Llama 3 Chatbot"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 109,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Running on local URL:  http://127.0.0.1:7919\n",
+      "\n",
+      "To create a public link, set `share=True` in `launch()`.\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "<div><iframe src=\"http://127.0.0.1:7919/\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": []
+     },
+     "execution_count": 109,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import ollama\n",
+    "import gradio as gr\n",
+    "def chat(question, history):\n",
+    "  history_format = []\n",
+    "  for human, assistant in history:\n",
+    "      history_format.append({\"role\": \"user\", \"content\": human})\n",
+    "      history_format.append({\"role\": \"assistant\", \"content\":assistant})\n",
+    "  history_format.append({'role': 'user', 'content': question})\n",
+    "\n",
+    "  messages=history_format\n",
+    "  stream = ollama.chat(model='llama3', messages=messages)\n",
+    "  #print(history_format)\n",
+    "  return stream['message']['content']\n",
+    "\n",
+    "# Gradio interface\n",
+    "assistant_icon = gr.Image(value=\"C:/Users/chris/Downloads/japanese-lama-ghibli-artstyle.jpeg\", width=32, height=32)  # adjust the size as needed\n",
+    "assistant_img = gr.Image(value=\"C:/Users/chris/Downloads/japanese-lama-ghibli-artstyle.jpeg\", elem_id=\"assistant_img\")\n",
+    "gr.ChatInterface(fn=chat, title=\"Chat Bot\",chatbot=gr.Chatbot(height=300)).launch()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

discollm.py

@@ -0,0 +1,18 @@
+import ollama
+import gradio as gr
+def chat(question, history):
+  history_format = []
+  for human, assistant in history:
+      history_format.append({"role": "user", "content": human})
+      history_format.append({"role": "assistant", "content":assistant})
+  history_format.append({'role': 'user', 'content': question})
+
+  messages=history_format
+  stream = ollama.chat(model='llama3', messages=messages)
+  #print(history_format)
+  return stream['message']['content']
+
+# Gradio interface
+assistant_icon = gr.Image(value="C:/Users/chris/Downloads/japanese-lama-ghibli-artstyle.jpeg", width=32, height=32)  # adjust the size as needed
+assistant_img = gr.Image(value="C:/Users/chris/Downloads/japanese-lama-ghibli-artstyle.jpeg", elem_id="assistant_img")
+gr.ChatInterface(fn=chat, title="Chat Bot",chatbot=gr.Chatbot(height=300)).launch()