add requirements

app.py

@@ -0,0 +1,102 @@
+import gradio as gr
+import random
+import json
+import os
+from datetime import datetime
+
+# This would be replaced with your actual SLM integration
+def generate_response(query, context, model_name):
+    """Placeholder function to generate response from an SLM"""
+    return f"This is a placeholder response from {model_name} based on query: {query} and context: {context}"
+
+def save_evaluation(query, context, model_a, model_b, response_a, response_b, preference):
+    """Save evaluation results to a JSON file"""
+    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    evaluation = {
+        "timestamp": timestamp,
+        "query": query,
+        "context": context,
+        "models": {
+            "left": model_a,
+            "right": model_b
+        },
+        "responses": {
+            "left": response_a,
+            "right": response_b
+        },
+        "preference": preference
+    }
+    
+    # Create directory if it doesn't exist
+    os.makedirs("evaluations", exist_ok=True)
+    
+    # Save to a file
+    with open(f"evaluations/eval_{timestamp.replace(' ', '_').replace(':', '-')}.json", "w") as f:
+        json.dump(evaluation, f, indent=2)
+    
+    return "Evaluation saved successfully!"
+
+def process_query(query, context, model_a="SLM-A", model_b="SLM-B"):
+    """Process query and generate responses from two models"""
+    # Generate responses
+    response_a = generate_response(query, context, model_a)
+    response_b = generate_response(query, context, model_b)
+    
+    # Randomly swap to avoid position bias
+    if random.random() > 0.5:
+        return response_a, response_b, model_a, model_b
+    else:
+        return response_b, response_a, model_b, model_a
+
+def submit_evaluation(query, context, response_left, response_right, preference, model_left, model_right):
+    """Submit and save the evaluation"""
+    if not preference:
+        return "Please select a preference before submitting."
+    
+    save_evaluation(query, context, model_left, model_right, response_left, response_right, preference)
+    return "Thank you for your evaluation!"
+
+with gr.Blocks(title="SLM-RAG Arena") as app:
+    gr.Markdown("# SLM-RAG Arena")
+    gr.Markdown("Compare responses from different models for RAG tasks.")
+    
+    with gr.Row():
+        with gr.Column():
+            query_input = gr.Textbox(label="Query", placeholder="Enter your query here...")
+            context_input = gr.Textbox(label="Context", placeholder="Enter context information here...", lines=5)
+            generate_btn = gr.Button("Generate Responses")
+        
+    # Hidden state variables
+    model_left = gr.State("")
+    model_right = gr.State("")
+    
+    with gr.Row():
+        with gr.Column():
+            gr.Markdown("### Response A")
+            response_left = gr.Textbox(label="", lines=10, interactive=False)
+        with gr.Column():
+            gr.Markdown("### Response B")
+            response_right = gr.Textbox(label="", lines=10, interactive=False)
+    
+    with gr.Row():
+        preference = gr.Radio(
+            choices=["Prefer Left", "Tie", "Prefer Right", "Neither"],
+            label="Which response do you prefer?"
+        )
+    
+    submit_btn = gr.Button("Submit Evaluation")
+    result = gr.Textbox(label="Result")
+    
+    generate_btn.click(
+        process_query, 
+        inputs=[query_input, context_input],
+        outputs=[response_left, response_right, model_left, model_right]
+    )
+    
+    submit_btn.click(
+        submit_evaluation,
+        inputs=[query_input, context_input, response_left, response_right, preference, model_left, model_right],
+        outputs=[result]
+    )
+
+app.launch()

requirements.txt

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+python>=3.10
+transformers>=4.51.0
+pandas>=2.2.3
+accelerate>=1.6.0
+numpy==1.26.4
+openai>=1.60.2
+torch>=2.5.1
+tqdm==4.67.1