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import transformers
import torch
import gradio as gr
# Load the chatbot model
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
# Function to calculate scores and rankings
def calculate_ranking(data):
for institution in data:
institution["Total"] = (
institution["TLR"] + institution["GO"] + institution["OI"] + institution["PR"]
)
ranked_data = sorted(data, key=lambda x: x["Total"], reverse=True)
for rank, institution in enumerate(ranked_data, start=1):
institution["Rank"] = rank
return ranked_data
# Chatbot function with ranking logic
def chatbot_response(user_message):
if "rank" in user_message.lower():
# Example data for ranking
example_data = [
{"Institution": "A", "TLR": 70, "GO": 85, "OI": 90, "PR": 75},
{"Institution": "B", "TLR": 80, "GO": 88, "OI": 85, "PR": 90},
{"Institution": "C", "TLR": 65, "GO": 80, "OI": 70, "PR": 60},
]
ranked_data = calculate_ranking(example_data)
response = "Here are the ranks of the institutions:\n"
for institution in ranked_data:
response += f"Rank {institution['Rank']}: {institution['Institution']} (Total Score: {institution['Total']})\n"
return response
else:
# Generate chatbot response from model
outputs = pipeline(
user_message,
max_new_tokens=256,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
return outputs[0]["generated_text"]
# Gradio interface
def build_gradio_ui():
with gr.Blocks() as demo:
gr.Markdown("## Chatbot with Hugging Face Spaces")
gr.Markdown("Type a message to interact with the chatbot! (Ask about institution rankings too!)")
with gr.Row():
user_input = gr.Textbox(label="Your Message", placeholder="Type your message here...")
chatbot_output = gr.Textbox(label="Chatbot Response", interactive=False)
submit_button = gr.Button("Send")
submit_button.click(chatbot_response, inputs=[user_input], outputs=[chatbot_output])
return demo
# Launch the Gradio app
demo = build_gradio_ui()
if __name__ == "__main__":
demo.launch()
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