Upload 2 files

app.py

@@ -0,0 +1,67 @@
+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()

requirements.txt

@@ -0,0 +1,3 @@
+gradio
+transformers
+torch