app.py

import gradio as gr
import openai
import json
from pinecone import Pinecone
pc = Pinecone(api_key='72f7d24d-c080-4ce6-a060-a489713284cb')
index_name = 'imdb-vector'
# connect to index
index = pc.Index(index_name)
# get api key from platform.openai.com
openai.api_key = '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'
embed_model = "text-embedding-ada-002"
from openai import OpenAI
client = OpenAI(api_key=openai.api_key)
def generate_context(text):
    body=json.dumps({"inputText": text})
    res = client.embeddings.create(
        input=[text],
        model=embed_model
    ).data[0].embedding
    result = index.query(vector=res, top_k=7, include_metadata=True)
    contexts = []
    contexts = contexts + [x['metadata']['text'] for x in result['matches']]
    return contexts
    
def invoke_openai(prompt):
    sys_prompt = "You are a helpful assistant that always answers questions."
    # query text-davinci-003
    res = client.chat.completions.create(
        model='gpt-3.5-turbo',
        messages=[
            {"role": "system", "content": sys_prompt},
            {"role": "user", "content": prompt}
        ],
        temperature=0
    )
    return res.choices[0].message.content
    
def build_prompt(message,history):
    context=generate_context(message)
    messages=[]
    prompt=f'Context - {context}\nBased on the above context, answer this question - {message}'
    print(prompt)
    return invoke_openai(prompt) 
     

iface = gr.ChatInterface(build_prompt, chatbot=gr.Chatbot(height=300), textbox=gr.Textbox(placeholder="Ask me a question", container=False, scale=7), title="IMDB Sentiment Data Analysis Chat", examples = ["Which is most postive review for a movie?", "What is the most reviewed movie?"], theme="soft", cache_examples=False, retry_btn=None, undo_btn="Delete Previous", clear_btn="Clear",)
iface.launch(share=True)