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import os
import textwrap
from dotenv import load_dotenv
import gradio as gr
from haystack import Pipeline
from haystack.utils import Secret
from haystack.components.builders import PromptBuilder
from haystack.components.generators import OpenAIGenerator
import pandas as pd
load_dotenv()
MODEL = "microsoft/Phi-3-mini-4k-instruct"
# Load the CSV file
df = pd.read_csv("dataset.csv")
# Set up components
prompt_template = """
Based on the Indian Union Budget data for FY 21-22 to 23-24:
{{budget_data}}
Answer the given question: {{query}}
Answer:
"""
prompt_builder = PromptBuilder(template=prompt_template)
llm = OpenAIGenerator(
api_key=Secret.from_env_var("MONSTER_API_KEY"),
api_base_url="https://llm.monsterapi.ai/v1/",
model=MODEL,
generation_kwargs={"max_tokens": 512}
)
pipeline = Pipeline()
pipeline.add_component("prompt", prompt_builder)
pipeline.add_component("llm", llm)
pipeline.connect("prompt.prompt", "llm.prompt")
# Function to handle the query
# def answer_query(query):
# # Convert DataFrame to string representation
# budget_data = df.to_string()
# result = pipeline.run({"prompt": {"budget_data": budget_data, "query": query}})
# return result["llm"]["replies"][0]
def answer_query(query):
try:
# Select a subset of the data (adjust as needed)
sample_data = df.sample(n=10).to_string()
# Truncate the data if it's too long
budget_data = textwrap.shorten(sample_data, width=1000, placeholder="...")
result = pipeline.run({"prompt": {"budget_data": budget_data, "query": query}})
return result["llm"]["replies"][0]
except Exception as e:
return f"An error occurred: {str(e)}"
# Gradio interface
def chat_interface(query):
return answer_query(query)
with gr.Blocks() as demo:
gr.Markdown("# Indian 2024 Budget Chatbot")
query_input = gr.Textbox(label="Enter Your Question")
submit_button = gr.Button("Get Answer")
output_text = gr.Textbox(label="Answer", interactive=False)
submit_button.click(fn=chat_interface, inputs=[query_input], outputs=output_text)
# Run the app locally
if __name__ == "__main__":
demo.launch() |