User Interface Added

.github/workflows/main.yaml

@@ -0,0 +1,98 @@
+name: workflow
+
+on:
+  push:
+    branches:
+      - main
+    paths-ignore:
+      - 'README.md'
+
+permissions:
+  id-token: write
+  contents: read
+
+jobs:
+  integration:
+    name: Continuous Integration
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Code
+        uses: actions/checkout@v3
+
+      - name: Lint code
+        run: echo "Linting repository"
+
+      - name: Run unit tests
+        run: echo "Running unit tests"
+
+  build-and-push-ecr-image:
+    name: Continuous Delivery
+    needs: integration
+    runs-on: ubuntu-latest
+    steps:
+      - name: Checkout Code
+        uses: actions/checkout@v3
+
+      - name: Install Utilities
+        run: |
+          sudo apt-get update
+          sudo apt-get install -y jq unzip
+      - name: Configure AWS credentials
+        uses: aws-actions/configure-aws-credentials@v1
+        with:
+          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
+          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+          aws-region: ${{ secrets.AWS_REGION }}
+
+      - name: Login to Amazon ECR
+        id: login-ecr
+        uses: aws-actions/amazon-ecr-login@v1
+
+      - name: Build, tag, and push image to Amazon ECR
+        id: build-image
+        env:
+          ECR_REGISTRY: ${{ steps.login-ecr.outputs.registry }}
+          ECR_REPOSITORY: ${{ secrets.ECR_REPOSITORY_NAME }}
+          IMAGE_TAG: latest
+        run: |
+          # Build a docker container and
+          # push it to ECR so that it can
+          # be deployed to ECS.
+          docker build -t $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG .
+          docker push $ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG
+          echo "::set-output name=image::$ECR_REGISTRY/$ECR_REPOSITORY:$IMAGE_TAG"
+          
+          
+  Continuous-Deployment:
+    needs: build-and-push-ecr-image
+    runs-on: self-hosted
+    steps:
+      - name: Checkout
+        uses: actions/checkout@v3
+
+      - name: Configure AWS credentials
+        uses: aws-actions/configure-aws-credentials@v1
+        with:
+          aws-access-key-id: ${{ secrets.AWS_ACCESS_KEY_ID }}
+          aws-secret-access-key: ${{ secrets.AWS_SECRET_ACCESS_KEY }}
+          aws-region: ${{ secrets.AWS_REGION }}
+
+      - name: Login to Amazon ECR
+        id: login-ecr
+        uses: aws-actions/amazon-ecr-login@v1
+
+
+      - name: Pull latest images
+        run: |
+         docker pull ${{secrets.AWS_ECR_LOGIN_URI}}/${{ secrets.ECR_REPOSITORY_NAME }}:latest
+         
+      # - name: Stop and remove container if running
+      #   run: |
+      #    docker ps -q --filter "name=chatbot" | grep -q . && docker stop texts && docker rm -fv chatbot
+
+      - name: Run Docker Image to serve users
+        run: |
+         docker run -d -p 8080:8080 --name=chatbot -e 'AWS_ACCESS_KEY_ID=${{ secrets.AWS_ACCESS_KEY_ID }}' -e 'AWS_SECRET_ACCESS_KEY=${{ secrets.AWS_SECRET_ACCESS_KEY }}' -e 'AWS_REGION=${{ secrets.AWS_REGION }}'  ${{secrets.AWS_ECR_LOGIN_URI}}/${{ secrets.ECR_REPOSITORY_NAME }}:latest
+      - name: Clean previous images and containers
+        run: |
+         docker system prune -f

README.md

@@ -1 +1,24 @@
-# AI-Driven-Customer-Chatbot
+# AI-Driven-Customer-Support-Chatbot
+
+## Steps to run the project
+
+```bash
+Project reop: https://github.com/Shashank1202/AI-Driven-Customer-Chatbot.git
+```
+
+```bash
+conda create -m yourenv_name python=3.8 -y
+```
+
+```bash
+conda activate yourenv_name
+```
+
+```bash
+pip install -r requirements.txt
+```
+
+```ini
+PINECONE_API_KEY="YOUR KEY"
+PINECONE_API_ENV="YOUR ENV"
+```

app.py

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+from flask import Flask, render_template, jsonify, request
+from src.helper import download_hugging_face_embeddings
+from langchain.vectorstores import Pinecone
+import pinecone
+from langchain.prompts import PromptTemplate
+from langchain.llms import CTransformers
+from langchain.chains import RetrievalQA
+from dotenv import load_dotenv
+from src.prompt import *
+import os
+
+#flask object initial
+app= Flask(__name__)
+
+PINECONE_API_KEY= os.environ.get('PINECONE_API_KEY')
+PINECONE_API_ENV= os.environ.get('PINECONE_API_ENV')
+
+embeddings= download_hugging_face_embeddings()
+
+pinecone.init(api_key= PINECONE_API_KEY,
+              environment= PINECONE_API_ENV)
+
+index_name= ""
+
+docsearch= Pinecone.from_existing_index(index_name, embeddings)
+
+PROMPT= PromptTemplate(template=prompt_template, input_variables=["context", "question"] )
+chain_type_kwargs= {"prompt": PROMPT}
+
+llm= CTransformers(model="model/llama-2-7b-chat.ggmlv3.q4_0.bin",
+                   model_type="llama",
+                   config={'max_new_tokens': 512,
+                            'temperature': 0.8})
+
+qa= RetrievalQA.from_chain_type(
+    llm= llm,
+    chain_type= "stuff",
+    retriever=docsearch.as_retriever(search_kwargs={'k':2}),
+    return_source_documents= True,
+    chain_type_kwargs= chain_type_kwargs
+)
+
+
+@app.route("/")
+def index():
+    return render_template('chat.html')
+
+@app.route("/get", methods=["GET", "POST"])
+def chat():
+    msg= request.form["msg"]
+    input= msg
+    print(input)
+    result= qa({"query": input})
+    print=("Response", result["result"])
+    return str(result["result"])
+
+if __name__=="__main__":
+    app.run(debug= True)

dockerfile

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+FROM python:3.8-slim-buster
+
+
+RUN apt update -y && apt install awscli -y
+
+WORKDIR /app
+
+COPY . /app
+
+RUN pip install -r requirement.txt
+
+CMD ["python3","app.py"]

model/instruction.txt

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+## Download the Model
+
+llama-2-7b-chat.ggmlv3.q4_0.bin
+
+## From Hugging Face

requirements.txt

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+ctransformers==0.2.5
+sentence-transformers==2.2.2
+pinecone-client
+langchain
+flask
+pypdf
+pinecone-client
+python-dotenv
+-e .