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import yaml
import gradio as gr
from typing import List
from llm_client import GeminiClient
from pinecone_index import PinceconeIndex
SYSTEM_MESSAGE = "Give a precise answer to the question based on only the \
context and evidence and do not be verbose."
TOP_K = 1
def format_prompt(question: str, evidence: List[str]):
evidence_string = ""
for i, ev in enumerate(evidence):
evidence_string = evidence_string.join(f"\n Evidence {i+1}: {ev}")
print(f"evidence string - {evidence_string}")
content = f"{SYSTEM_MESSAGE} \
\n ### Question:{question} \
\n ### Evidence: {evidence_string} \
\n ### Response:"
return content
if __name__ == "__main__":
config_path = "config.yml"
with open("config.yml", "r") as file:
config = yaml.safe_load(file)
print(config)
data_path = config["paths"]["data_path"]
project = config["paths"]["project"]
index_name = config["pinecone"]["index-name"]
embedding_model = config["sentence-transformers"]["model-name"]
embedding_dimension = config["sentence-transformers"]["embedding-dimension"]
index = PinceconeIndex(index_name, embedding_model)
index.connect_index(embedding_dimension, False)
gemini_client = GeminiClient()
def get_answer(question: str):
evidence = index.query(question, top_k=TOP_K)
prompt_with_evidence = format_prompt(question, evidence)
print(prompt_with_evidence)
response = gemini_client.generate_text(prompt_with_evidence)
final_output = [response] + evidence
return final_output
context_outputs = [gr.Textbox(label=f"Evidence {i+1}") for i in range(TOP_K)]
result_output = [gr.Textbox(label="Answer")]
gradio_outputs = result_output + context_outputs
gradio_inputs = gr.Textbox(placeholder="Enter your question...")
demo = gr.Interface(
fn=get_answer,
inputs=gradio_inputs,
# outputs=[gr.Textbox(label=f'Document {i+1}') for i in range(TOP_K)],
outputs=gradio_outputs,
title="GT Student Code of Conduct Bot",
description="Get LLM-powered answers to questions about the \
Georgia Tech Student Code of Conduct. The evidences are exerpts\
from the Code of Conduct.",
)
demo.launch()
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