Create app.py

app.py

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+import gradio as gr
+from huggingface_hub import InferenceClient
+import difflib  # for fuzzy matching
+from sentence_transformers import SentenceTransformer
+import torch
+import numpy as np
+
+# ==== Load knowledge ====
+with open("knowledge.txt", "r", encoding="utf-8") as f:
+    knowledge_text = f.read()
+
+chunks = [chunk.strip() for chunk in knowledge_text.split("\n\n") if chunk.strip()]
+
+embedder = SentenceTransformer('all-MiniLM-L6-v2')
+chunk_embeddings = embedder.encode(chunks, convert_to_tensor=True)
+
+def get_relevant_context(query, top_k=3):
+    query_embedding = embedder.encode(query, convert_to_tensor=True)
+    query_embedding = query_embedding / query_embedding.norm()
+    norm_chunk_embeddings = chunk_embeddings / chunk_embeddings.norm(dim=1, keepdim=True)
+    similarities = torch.matmul(norm_chunk_embeddings, query_embedding)
+    top_k_indices = torch.topk(similarities, k=top_k).indices.cpu().numpy()
+    return "\n\n".join([chunks[i] for i in top_k_indices])
+
+# ==== Model ====
+client = InferenceClient("google/gemma-2-2b-it")
+
+def is_driving_related(message):
+    driving_keywords = [
+        "drive", "driving", "permit", "car", "road", "lane", "traffic",
+        "license", "parallel", "park", "stop sign", "brake", "accelerate",
+        "merge", "intersection", "seatbelt", "speed limit", "turn signal",
+        "pulled over", "parking", "roundabout"
+    ]
+    message_words = message.lower().split()
+    for word in message_words:
+        for keyword in driving_keywords:
+            if difflib.SequenceMatcher(None, word, keyword).ratio() > 0.8:
+                return True
+    return False
+
+def respond(message, history):
+    if not history:
+        if not is_driving_related(message):
+            return "Hey there! I’m Drive Wise 🚗 — I can only help with driving topics like road rules, parking tips, or permit prep. What driving question do you have for me?"
+
+    messages = [{"role": "system", "content": """You are Drive Wise, a friendly and supportive AI chatbot designed to help new drivers learn essential driving skills and traffic laws. Your goal is to make learning to drive simple, confident, and stress-free."""}]
+
+    for user_msg, assistant_msg in history:
+        messages.append({"role": "user", "content": user_msg})
+        messages.append({"role": "assistant", "content": assistant_msg})
+
+    messages.append({"role": "user", "content": message})
+
+    response = ""
+    
+    # for msg in client.chat_completion(messages, max_tokens=500, temperature=.1, stream=True):
+    #     token = msg.choices[0].delta.content
+    #     if token:
+    #         response += token
+    # return response
+
+
+ # iterate through each message in the method
+    try:
+      for message in client.chat_completion(
+          messages,
+          max_tokens=500,
+          temperature=0.1,
+          stream=True
+      ):
+          # add the tokens to the output content
+          token = message.choices[0].delta.content # capture the most recent token
+          response += token # Add it to the response
+          yield response # yield the response
+
+    except Exception as e:
+        print(f"An error occurred: {e}")
+
+# === UI ===
+about_text = """
+## About this bot  
+This chatbot will help you learn more about driving!  
+Ask me about road rules, parking, your learner’s permit, or how to handle situations like being pulled over.
+"""
+
+with gr.Blocks() as demo:
+    gr.Markdown(about_text)
+
+    chatbot = gr.Chatbot(label="PERMIT TEST QUESTIONS")
+
+    with gr.Row():
+        msg = gr.Textbox(placeholder="Type your driving question here...")
+        send = gr.Button("Send")
+
+    def user_interaction(user_message, chat_history):
+        bot_message = respond(user_message, chat_history)
+        chat_history.append((user_message, bot_message))
+        return "", chat_history
+
+    send.click(user_interaction, inputs=[msg, chatbot], outputs=[msg, chatbot])
+    msg.submit(user_interaction, inputs=[msg, chatbot], outputs=[msg, chatbot])
+
+# Fix for localhost issue
+demo.launch(share=True)