app.py

from gradio_client import Client
import gradio as gr
import requests
from langchain.chains import RetrievalQA
import pinecone
from langchain.vectorstores import Pinecone
import os
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
import time

API_URL = "https://api-inference.huggingface.co/models/HuggingFaceH4/zephyr-7b-beta"
headers = {"Authorization": f"Bearer {os.environ.get('API_KEY')}"}
# retrieval = Client("https://ishaan-mital-ncert-helper-vector-db.hf.space/--replicas/149bl5mjn/")

embed_model_id = 'sentence-transformers/all-MiniLM-L6-v2'
# device = f'cuda:{cuda.current_device()}' if cuda.is_available() else 'cpu'

embed_model = HuggingFaceEmbeddings(
    model_name=embed_model_id,
    # model_kwargs={'device': device},
    # encode_kwargs={'device': device, 'batch_size': 32}
)


pinecone.init(
    api_key=os.environ.get('PINECONE_API_KEY'),
    environment=os.environ.get('PINECONE_ENVIRONMENT')
)

index_name = 'llama-rag'
index = pinecone.Index(index_name)
text_field = 'text'  # field in metadata that contains text content
docs = [
    "this is one document",
    "and another document"
]

embeddings = embed_model.embed_documents(docs)
if index_name not in pinecone.list_indexes():
    pinecone.create_index(
        index_name,
        dimension=len(embeddings[0]),
        metric='cosine'
    )
    # wait for index to finish initialization
    while not pinecone.describe_index(index_name).status['ready']:
        time.sleep(1)
vectorstore = Pinecone(
    index, embed_model.embed_query, text_field
)

def call_llm_api(input_text,context):
    payload = {
        "inputs": f'question: {input_text}, context: {context}'
            }
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()  # Adjust as needed based on your API response format
	
rag_pipeline = RetrievalQA.from_chain_type(
    llm=call_llm_api, chain_type='stuff',
    retriever=vectorstore.as_retriever()
)


def main(question):
    return rag_pipeline(question)
    # global chatbot
    # context = retrieval.predict(question, api_name = "/predict")
    # answer=call_llm_api(question,context)
    # # chatbot = answer[1]
    # return answer[0]

demo = gr.Interface(main, inputs = "text", outputs = "text")

if __name__ == "__main__":
    demo.launch()