app.py

import gradio as gr
import requests
import os
from dotenv import load_dotenv

# Load environment variables from .env file
load_dotenv()

API_URL = "https://api-inference.huggingface.co/models/deepset/roberta-base-squad2"
headers = {"Authorization": f"Bearer {os.environ['HUGGINGFACE_API_KEY']}"}

# Initialize an empty list to store conversation history
conversation_history = []

def query(payload):
    response = requests.post(API_URL, headers=headers, json=payload)
    return response.json()

def answer_question(question, context):
    # Use the Hugging Face Inference API to get the answer
    output = query({
        "inputs": {
            "question": question,
            "context": context
        },
    })
    
    # Extract the answer from the API response
    answer = output.get('answer', 'Sorry, I couldn\'t find an answer.')
    
    # Add the Q&A to the conversation history
    conversation_history.append(f"Human: {question}")
    conversation_history.append(f"AI: {answer}")
    
    # Return the full conversation history as a string
    return "\n".join(conversation_history)

# Create the Gradio interface
iface = gr.Interface(
    fn=answer_question,
    inputs=[
        gr.Textbox(label="Question", placeholder="Ask your question here..."),
        gr.Textbox(label="Context", placeholder="Provide context here...", lines=5)
    ],
    outputs=gr.Textbox(label="Conversation", lines=10),
    title="IEEE USJ Student Branch Copilot",
    description="Ask questions based on the provided context.",
    theme="default"
)

# Launch the app
iface.launch()