app.py

import gradio as gr
from huggingface_hub import InferenceClient
from sentence_transformers import SentenceTransformer
import torch



# Theme
theme = gr.themes.Soft(
   primary_hue="gray",
   secondary_hue="red",
   neutral_hue="orange"
)



custom_css = """
:root {
   --background-fill-primary: #1A1A1A !important;
}
.dark {
   --background-fill-primary: ##1A1A1A !important;
}
"""



# Load research file
with open("research.txt", "r", encoding="utf-8") as file:
   research_text = file.read()



# Preprocess text
def preprocess_text(text):
   cleaned_text = text.strip()
   chunks = cleaned_text.split("\n")
   cleaned_chunks = [chunk.strip() for chunk in chunks if chunk.strip() != ""]
   return cleaned_chunks



cleaned_chunks = preprocess_text(research_text)



# Create embeddings
model = SentenceTransformer('all-MiniLM-L6-v2')



def create_embeddings(text_chunks):
   chunk_embeddings = model.encode(text_chunks, convert_to_tensor=True)
   return chunk_embeddings



chunk_embeddings = create_embeddings(cleaned_chunks)



# Get top chunks
def get_top_chunks(query, chunk_embeddings, text_chunks):
   query_embedding = model.encode(query, convert_to_tensor=True)
   query_embedding_normalized = query_embedding / query_embedding.norm()
   chunk_embeddings_normalized = chunk_embeddings / chunk_embeddings.norm(dim=1, keepdim=True)
   similarities = torch.matmul(chunk_embeddings_normalized, query_embedding_normalized)
   top_indices = torch.topk(similarities, k=3).indices
   top_chunks = [text_chunks[i] for i in top_indices]
   return top_chunks



# Inference client
client = InferenceClient("Qwen/Qwen2.5-72B-Instruct")



def respond(message, history):
   top_results = get_top_chunks(message, chunk_embeddings, cleaned_chunks)
   str_top_results = '\n'.join(top_results)
   messages = [
       {'role': 'system', 'content': f'You are a chatbot. Complete all your sentences, be concise, and do not cut yourself off. The word limit is 100 words. Start off by giving a car in a complete, informitive sentence, and then if prompted by the user provide more information like engine type,  problems, solutions, transmission, drivetrain,pros, cons,etc. Base your response on the provided context:\n{str_top_results}'}
   ]
   if history:
       messages.extend(history)
   messages.append({'role': 'user', 'content': message})



   response = client.chat_completion(
       messages,
       max_tokens=1000,
       temperature=0.2
   )
   return response['choices'][0]['message']['content'].strip()



def display_image():
   return "banner.jpg"



# Explore Page Info
def show_info(topic):
   responses = {
       "Fastest Nissan GTR Models": "1. GTR Nismo (R35)\n2. GTR SpecV (R35)\n3. GTR Track Edition (R35)\n4. GTR R34 V-Spec II Nür\n5. GTR R33 V-Spec",
       "Most Powerful Nissan Engines": "1. VR38DETT (GTR R35 Nismo)\n2. RB26DETT (GTR R34)\n3. VQ38DETT (Frontier/Patrol Prototype)\n4. VK56DETT (GT-R LM Nismo LMP1)\n5. RB30DET (Custom R31 Skyline Builds)",
       "Quickest 0–60 Times": "1. Nissan GTR Nismo – 2.5 sec\n2. Lucid Air Sapphire - 1.86 sec\n3. Porsche 911 Turbo S – 2.2 sec\n4. Ferrari SF90 Stradale – 2.3 sec\n5. Lamborghini Huracán Performante – 2.4 sec",
       "Best Handling Sports Cars": "1. Mazda MX-5 Miata\n2. Porsche Cayman GT4\n3. Chevrolet Corvette Z06\n4. Nissan GTR R35\n5. BMW M2 Competition",
       "Top Speed Monsters": "1. Bugatti Chiron Super Sport 300+ – 304 mph\n2. Koenigsegg Jesko Absolut – 330+ mph (theoretical)\n3. Hennessey Venom F5 – 311 mph\n4. SSC Tuatara – 282 mph (verified)\n5. Bugatti Mistral - 282 mph"
   }
   return responses.get(topic, "Select a category to see the corresponding cars.")







   content = resources.get(career)
   if not content:
       return "Select a career to see resources.", ""



   link_html = "<ul>"
   for label, url in content["links"]:
       link_html += f'<li><strong>{label}</strong>: <a href="{url}" target="_blank">{url}</a></li>'
   link_html += "</ul>"



   college_html = ""
   if "college" in content:
       college = content["college"]
       college_html += "<p><strong>College & Classes</strong></p><ul>"
       college_html += f"<li><em>Common Major(s):</em> {college['major']}</li>"
       classes_list = college.get("classes", [])
       if isinstance(classes_list, list):
           classes_html = ", ".join(classes_list)
       else:
           classes_html = str(classes_list)
       college_html += f"<li><em>Helpful College Classes:</em> {classes_html}</li>"
       college_html += "</ul>"



   # No video iframe, just empty string for second output
   return link_html + college_html, ""



# UI Layout
with gr.Blocks(theme=theme, css=custom_css) as chatbot:
   gr.Image(display_image)



   with gr.Tab("ChatBot"):
       gr.ChatInterface(
           respond,
           type="messages",
           title="Hi, I'm RB26 AI!",
           textbox=gr.Textbox(placeholder="Desribe your car's problems, and I can provide steps to solve it! If you don't have any problems, and are here to learn about Nissan Skylines and GT-Rs, you are welcome to type your questions"),
           description='This tool provides information on Nissan Skylines and GT-Rs.'
       )






   with gr.Tab("Explore Now"):  # ✅ Changed title
       gr.Markdown("### Explore More Nissan GT-R Records")
       dropdown_explore = gr.Dropdown(
           choices=[
               "Fastest Nissan GTR Models",
               "Most Powerful Nissan Engines",
               "Quickest 0–60 Times",
               "Best Handling Sports Cars",
               "Top Speed Monsters"
           ],
           label="Choose a Category"
       )
       output_explore = gr.Markdown()



       dropdown_explore.change(fn=show_info, inputs=dropdown_explore, outputs=output_explore)










chatbot.launch()