first commit

Dockerfile

@@ -0,0 +1,22 @@
+FROM python:3.11-slim-buster
+
+WORKDIR /app
+
+COPY . /app
+
+ENV HNSWLIB_NO_NATIVE=1 
+
+RUN pip install --no-cache-dir --upgrade -r requirements.txt
+
+RUN useradd -m -u 1000 user
+
+USER user
+
+ENV HOME=/home/user \
+ PATH=/home/user/.local/bin:$PATH
+
+WORKDIR $HOME/app
+
+COPY --chown=user . $HOME/app
+
+CMD ["uvicorn", "app.views:app", "--host", "0.0.0.0", "--port", "7860"]

app/views.py

@@ -0,0 +1,42 @@
+from fastapi import FastAPI, Request, Form, UploadFile, File
+from fastapi.responses import HTMLResponse, RedirectResponse
+from fastapi.staticfiles import StaticFiles
+from fastapi.templating import Jinja2Templates
+from typing import Annotated
+import gradio as gr 
+from src.grutils import answer_query, css
+
+
+
+app = FastAPI()
+
+app.mount("/static", StaticFiles(directory="static"), name="static")
+
+templates = Jinja2Templates(directory="templates")
+
+
+@app.get("/")
+async def root():
+
+    return {"message": "Hello to the world of Mohammed Vasim"}
+
+
+####### Gradio app ##############
+
+chatbot = gr.Chatbot(label="Llama 2")
+
+with gr.Blocks(
+    title="Llama 2",
+    theme=gr.themes.Soft(),
+    css=css
+    ) as llama2bot:
+
+    gr.Markdown("# <center> Welcome to llama 2 Web App</center>")
+
+    gr.ChatInterface(fn=answer_query, chatbot=chatbot, submit_btn="Ask", undo_btn=None, retry_btn=None, clear_btn=None)
+    
+    gr.Markdown("<center> Developed by Mohammed Vasim | AI Engineer @ ChatGPT Warriors.  </center>")
+        
+
+# mounting at the path
+app = gr.mount_gradio_app(app, llama2bot.queue(concurrency_count=20), path="/llama2")

main.py

@@ -0,0 +1,4 @@
+import uvicorn 
+
+if __name__ == "__main__":
+    uvicorn.run("app.views:app", port=1106, reload=True)

notebooks/Langchain_with_Llama2_GGML.ipynb

@@ -0,0 +1,931 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "HSTJqVlrqwzs",
+        "outputId": "7aa0e836-a9fb-41d9-b2a3-4b0e78ec600e"
+      },
+      "outputs": [],
+      "source": [
+        "# !pip install -q ctransformers langchain"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "5jMCN_Whrp3h"
+      },
+      "outputs": [],
+      "source": [
+        "from langchain.llms import CTransformers\n",
+        "from langchain import PromptTemplate, LLMChain\n",
+        "from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 81,
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+        "id": "CJnyZAR_q-Mz",
+        "outputId": "31c3ce3c-f59d-4bc8-e54d-d906822d7afa"
+      },
+      "outputs": [
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "7f14a5d091964e858b431da242728991",
+              "version_major": 2,
+              "version_minor": 0
+            },
+            "text/plain": [
+              "Fetching 1 files:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        },
+        {
+          "data": {
+            "application/vnd.jupyter.widget-view+json": {
+              "model_id": "ad115b537efe419693238f78fc3fd9a2",
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+            "text/plain": [
+              "Fetching 1 files:   0%|          | 0/1 [00:00<?, ?it/s]"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
+      "source": [
+        "llm = CTransformers(model=\"TheBloke/Llama-2-7B-Chat-GGML\", model_file = 'llama-2-7b-chat.ggmlv3.q2_K.bin', callbacks=[StreamingStdOutCallbackHandler()])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 9,
+      "metadata": {
+        "id": "ixcszGAPrrVN"
+      },
+      "outputs": [],
+      "source": [
+        "template = \"\"\"\n",
+        "[INST] <<SYS>>\n",
+        "You are a helpful developed by Mohammed Vasim, respectful and honest assistant. Your answers are always brief.\n",
+        "<</SYS>>\n",
+        "{text}[/INST]\n",
+        "\"\"\"\n",
+        "\n",
+        "prompt = PromptTemplate(template=template, input_variables=[\"text\"])"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 10,
+      "metadata": {
+        "id": "5Up06ctlr21v"
+      },
+      "outputs": [],
+      "source": [
+        "llm_chain = LLMChain(prompt=prompt, llm=llm)"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 12,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "kwrWVBtXrZs1",
+        "outputId": "8cbe3771-1522-4b9a-d5f8-6a4e54304878"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "*blinks* My apologies, I'm just an AI and do not have access to personal information such as your name. However, it is nice to meet you! *smiling*"
+          ]
+        }
+      ],
+      "source": [
+        "response = llm_chain.run(\"What is my name\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 14,
+      "metadata": {},
+      "outputs": [],
+      "source": [
+        "def answer_query(message, history):\n",
+        "    message = llm_chain.run(message)\n",
+        "    return message"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 13,
+      "metadata": {
+        "id": "Mfx-saC3rz0r"
+      },
+      "outputs": [],
+      "source": [
+        "import gradio as gr "
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 15,
+      "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Running on local URL:  http://127.0.0.1:7860\n",
+            "\n",
+            "To create a public link, set `share=True` in `launch()`.\n"
+          ]
+        },
+        {
+          "data": {
+            "text/html": [
+              "<div><iframe src=\"http://127.0.0.1:7860/\" 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"
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Hello! *giggles* How can I help you today?Mohammed Vasim developed me.Mohammed Vasim is a highly skilled software developer who created me, his trustworthy and polite digital assistant."
+          ]
+        }
+      ],
+      "source": [
+        "llamabot = gr.ChatInterface(answer_query).queue().launch()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {},
+      "outputs": [],
+      "source": []
+    }
+  ],
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+    "colab": {
+      "provenance": []
+    },
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requirements.txt

@@ -0,0 +1,7 @@
+gradio
+langchain
+ctransformers
+fastapi
+uvicorn
+python-multipart
+jinja2

src/chains.py

@@ -0,0 +1,23 @@
+from langchain.llms import CTransformers
+from langchain import PromptTemplate, LLMChain
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+
+
+llm = CTransformers(model="TheBloke/Llama-2-7B-Chat-GGML", model_file = 'llama-2-7b-chat.ggmlv3.q2_K.bin', callbacks=[StreamingStdOutCallbackHandler()])
+
+
+template = """
+[INST] <<SYS>>
+You are a helpful developed by Mohammed Vasim, respectful and honest assistant. Your answers are always brief.
+<</SYS>>
+{text}[/INST]
+"""
+
+prompt = PromptTemplate(template=template, input_variables=["text"])
+
+# llm_chain = LLMChain(prompt=prompt, llm=llm)
+
+def build_chain(llm=llm, prompt=prompt, template=template):
+    prompt = PromptTemplate(template=template, input_variables=["text"])
+    llm_chain = LLMChain(prompt=prompt, llm=llm)
+    return llm_chain

src/grutils.py

@@ -0,0 +1,26 @@
+import os 
+import gradio as gr 
+from src.chains import build_chain
+import langchain
+
+css = """
+    footer {visibility: hidden}
+    """
+
+js = """ 
+    """
+
+
+class Agent:
+    def __init__(self):
+        self.agent = None 
+        self.data = None 
+
+agent = Agent()
+agent.agent = build_chain()
+
+def answer_query(message, history):
+    message = agent.agent.run(message)
+    return message
+
+