Initial repo for qna_app with and without redis

a7753969 · Sanjay Pant · a7753969 · a7753969 · a7753969 · a7753969
Commit a7753969 authored Mar 01, 2023 by Sanjay Pant
7 changed files
--- a/qna_app/Dockerfile
+++ b/qna_app/Dockerfile
+# Start with a base Python image
+FROM python:3.9-slim-buster
+
+# Copy files or folders from source to the dest path in the image's filesystem
+COPY requirements.txt ./
+
+# Execute any commands on top of the current image as a new layer and commit the results
+RUN pip install -r requirements.txt \
+ && rm -rf /root/.cache/pip
+
+# Copy files or folders from source to the dest path in the image's filesystem
+COPY . .
+
+EXPOSE 8080
+
+# The command that will be executed when a container is run from the image
+ENTRYPOINT ["python", "app.py"]
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--- a/qna_app/app.py
+++ b/qna_app/app.py
+import gradio as gr
+import openai
+
+Title = "Experience the Power of Knowledge with Smart QnA Bot"
+Description = "The smart QnA bot is a tool that can help you find answers to your questions quickly and easily. Using advanced algorithms and a vast knowledge base, the bot can provide comprehensive responses to a wide range of inquiries. However, the accuracy of the answers may vary, and they should always be cross-checked with other sources to ensure their reliability. Whether you're a student, a professional, or just someone seeking information, the QnA bot is a useful resource for exploring different topics and expanding your knowledge. Give it a try today and experience the convenience and efficiency of this smart tool."
+
+def openai_call(question, api_key):
+ openai.api_key = api_key
+ response = openai.Completion.create(
+ model="text-davinci-003",
+ prompt=question,
+ temperature=0,
+ max_tokens=100
+ )
+ return response.choices[0].text
+
+ 
+
+open_ai_key = gr.inputs.Textbox(label="Enter the Open AI API Key",type="password")
+open_ai_question = gr.inputs.Textbox(lines=10, label="Enter the question") 
+open_ai_summary = gr.outputs.Label(label="Answer")
+demo = gr.Interface(title=Title, description=Description, fn=openai_call,inputs=[open_ai_question,open_ai_key], outputs=[open_ai_summary],css="footer {visibility: hidden}")
+
+demo.launch(server_name="0.0.0.0", server_port=8080) 
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--- a/qna_app/requirements.txt
+++ b/qna_app/requirements.txt
+gradio
+openai
+redis
\ No newline at end of file
--- a/qna_app_redis/Dockerfile
+++ b/qna_app_redis/Dockerfile
+# Start with a base Python image
+FROM python:3.9-slim-buster
+
+# Copy files or folders from source to the dest path in the image's filesystem
+COPY requirements.txt ./
+
+# Execute any commands on top of the current image as a new layer and commit the results
+RUN pip install -r requirements.txt \
+ && rm -rf /root/.cache/pip
+
+# Copy files or folders from source to the dest path in the image's filesystem
+COPY . .
+
+
+# The command that will be executed when a container is run from the image
+ENTRYPOINT ["python", "app_redis.py"]
\ No newline at end of file
--- a/qna_app_redis/app.py
+++ b/qna_app_redis/app.py
+import gradio as gr
+import openai
+import redis
+
+Title = "Experience the Power of Knowledge with Smart QnA Bot with Redis"
+Description = "The smart QnA bot is a tool that can help you find answers to your questions quickly and easily. Using advanced algorithms and a vast knowledge base, the bot can provide comprehensive responses to a wide range of inquiries. However, the accuracy of the answers may vary, and they should always be cross-checked with other sources to ensure their reliability. Whether you're a student, a professional, or just someone seeking information, the QnA bot is a useful resource for exploring different topics and expanding your knowledge. Give it a try today and experience the convenience and efficiency of this smart tool."
+
+# Connect to Redis
+redis_client = redis.Redis(host='redis', port=6379, db=0)
+
+def openai_call(question, api_key):
+ # Check if the answer is already in Redis cache
+ cached_answer = redis_client.get(question)
+ if cached_answer:
+ return cached_answer.decode('utf-8')
+
+ # If answer is not in cache, call OpenAI API
+ openai.api_key = api_key
+ response = openai.Completion.create(
+ model="text-davinci-003",
+ prompt=question,
+ temperature=0,
+ max_tokens=100
+ )
+ answer = response.choices[0].text
+
+ # Store the answer in Redis cache for future requests
+ redis_client.set(question, answer)
+
+ return answer
+
+open_ai_key = gr.inputs.Textbox(label="Enter the Open AI API Key",type="password")
+open_ai_question = gr.inputs.Textbox(lines=10, label="Enter the question") 
+open_ai_answer = gr.outputs.Label(label="Answer")
+demo = gr.Interface(title=Title, description=Description, fn=openai_call,inputs=[open_ai_question,open_ai_key], outputs=[open_ai_answer],css="footer {visibility: hidden}")
+
+demo.launch(server_name="0.0.0.0", server_port=8081)
--- a/qna_app_redis/docker-compose.yml
+++ b/qna_app_redis/docker-compose.yml
+version: '3'
+services:
+ gradio:
+ image: "datasciencedojo/app-openai:latest"
+ container_name: gradiov1
+ build:
+ context: .
+ dockerfile: Dockerfile_Redis
+
+ ports:
+ - "80:8081"
+ depends_on:
+ - redis
+ environment:
+ - REDIS_HOST=redis
+ - REDIS_PORT=6379
+ redis:
+ image: "redis:latest"
+ container_name: redisv1
+ ports:
+ - "6379:6379"
+ volumes:
+ - ./data:/data
+ command: redis-server --appendonly yes --dir /data
--- a/qna_app_redis/requirements.txt
+++ b/qna_app_redis/requirements.txt
+gradio
+openai
+redis
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