app.py

import gradio as gr
from sentence_transformers import SentenceTransformer, util
import torch

# Load the sentence-transformer model
model = SentenceTransformer('all-MiniLM-L6-v2')

# Expanded FAQ dataset with more diverse responses
faq_data = [
    ("What is Hugging Face?", "Hugging Face is a company specializing in AI and machine learning, known for their open-source models and datasets."),
    ("How does Hugging Face help in AI?", "Hugging Face provides tools, libraries, and pre-trained models to make machine learning easier and more accessible."),
    ("What is machine learning?", "Machine learning is a subset of AI that enables computers to learn from data and improve over time without being explicitly programmed."),
    ("What is a transformer model?", "A transformer model is a deep learning model that uses attention mechanisms to process and generate sequences of data, such as text or speech."),
    ("Can I use Hugging Face models in production?", "Yes, Hugging Face provides tools and frameworks like `transformers` for deploying models into production environments."),
    ("What is RAG?", "Retrieval-Augmented Generation (RAG) combines pre-trained models with retrieval systems to answer questions using both the knowledge from the model and external documents."),
    ("What is AI?", "Artificial Intelligence (AI) is the simulation of human intelligence in machines, enabling them to perform tasks that typically require human cognition."),
    ("Tell me a joke", "Why don't skeletons fight each other? They don’t have the guts!"),
    ("What is the capital of France?", "The capital of France is Paris."),
    ("How can I contact support?", "You can contact support via our website or email for assistance."),
    ("What's the weather like today?", "Sorry, I don't have access to real-time data, but I suggest checking a weather app for the latest updates.")
]

# Encode the FAQ dataset
corpus = [item[0] for item in faq_data]  # Questions only
answers = {item[0]: item[1] for item in faq_data}  # Map questions to their answers
corpus_embeddings = model.encode(corpus, convert_to_tensor=True)

# Function to retrieve the most relevant FAQ based on user query
def retrieve(query):
    query_embedding = model.encode(query, convert_to_tensor=True)
    cosine_scores = util.pytorch_cos_sim(query_embedding, corpus_embeddings)[0]
    top_result_idx = torch.argmax(cosine_scores)
    top_score = cosine_scores[top_result_idx].item()

    # Threshold for similarity score
    if top_score < 0.5:  # Adjust threshold as needed
        return "I didn’t understand that. Could you try asking something else?"

    # Return the most relevant answer based on the query
    return answers[corpus[top_result_idx]]

# Create Gradio interface
iface = gr.Interface(fn=retrieve, inputs="text", outputs="text", live=True, title="RAG AI Bot", description="Ask me anything related to Hugging Face or general knowledge!")

# Launch the Gradio interface
iface.launch()