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import json
import gradio as gr
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
from huggingface_hub import InferenceClient
# 🔹 Load JSON Data with Colorado Food Stamp Information
DATA_FILE = "colorado_foodstamps.json"
def load_json_data():
try:
with open(DATA_FILE, "r", encoding="utf-8") as f:
return json.load(f)
except FileNotFoundError:
return {"error": "Data file not found! Please upload a valid JSON file."}
data = load_json_data()
# 🔹 Initialize FAISS for Searching Relevant Answers
model = SentenceTransformer("multi-qa-mpnet-base-dot-v1")
def create_faiss_index(data):
texts = list(data.values())
embeddings = np.array([model.encode(text) for text in texts])
index = faiss.IndexFlatL2(embeddings.shape[1])
index.add(embeddings)
return index, texts
index, texts = create_faiss_index(data)
# 🔹 Function to Search FAISS for Relevant Answers
def search_faiss(query, top_k=1):
query_embedding = model.encode(query).reshape(1, -1)
distances, indices = index.search(query_embedding, top_k)
return texts[indices[0][0]] if indices[0][0] < len(texts) else "No relevant information found."
# 🔹 Hugging Face API for Additional Responses
client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
def get_huggingface_response(query):
messages = [{"role": "system", "content": "Provide accurate food stamp information for Colorado."},
{"role": "user", "content": query}]
response = ""
for message in client.chat_completion(messages, max_tokens=512, stream=True, temperature=0.7, top_p=0.95):
response += message.choices[0].delta.content
return response
# 🔹 Main Chatbot Function
def chatbot_response(message, history):
relevant_info = search_faiss(message)
if "No relevant information found." not in relevant_info:
return relevant_info
return get_huggingface_response(message)
# 🔹 Gradio Chat Interface
demo = gr.ChatInterface(chatbot_response, textbox=gr.Textbox(placeholder="Ask about Colorado food stamps..."))
demo.launch()
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