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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: | |
| data = json.load(f) | |
| # Ensure data is a dictionary, not a list | |
| if isinstance(data, list): | |
| data = {str(i): str(entry) for i, entry in enumerate(data)} | |
| # Convert all values to strings | |
| data = {key: str(value) for key, value in data.items()} | |
| return data | |
| except (FileNotFoundError, ValueError) as e: | |
| return {"error": f"Data loading issue: {e}"} | |
| data = load_json_data() | |
| # 🔹 Initialize FAISS for Searching Relevant Answers | |
| model = SentenceTransformer("all-MiniLM-L6-v2") # Faster with good accuracy | |
| 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: | |
| # Summarize and clarify the retrieved policy text using Hugging Face LLM | |
| user_query_with_context = f""" | |
| The user is asking: {message} | |
| The most relevant policy information retrieved is: | |
| {relevant_info} | |
| Please summarize this information in a clear, concise, and user-friendly way for someone inquiring about Colorado food stamps. | |
| Provide the most recent information you can. If the most relevant policy information does not seem to apply do your best to answer the question accurately but make it clear you are not pulling the reply from the SNAP policy CCR and at that point you can encourage they try searching other keywords to possibly find better policy information. | |
| """ | |
| return get_huggingface_response(user_query_with_context) | |
| return get_huggingface_response(message) | |
| # 🔹 Gradio Chat Interface | |
| demo = gr.ChatInterface(chatbot_response, textbox=gr.Textbox(placeholder="Ask about Colorado food stamps...")) | |
| demo.launch() | |