Add Gradio interface for comparing two models

README.md

@@ -12,3 +12,17 @@ short_description: compare unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bi
 ---
 
 Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+
+# Model Comparison Space
+
+This Space allows you to compare the responses of two models:
+- **Original Model**: `unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit`
+- **Fine-Tuned Model**: `kas1/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit-John1`
+
+## Features
+- Enter a single prompt and compare responses side by side.
+- Upload a JSON file with multiple questions and compare batch results.
+
+## How to Use
+1. Go to the "Single Prompt" tab and enter a question.
+2. Alternatively, upload a JSON file in the "Batch Testing" tab.

app.py

@@ -0,0 +1,78 @@
+import gradio as gr
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the original model
+original_model = AutoModelForCausalLM.from_pretrained("unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit")
+original_tokenizer = AutoTokenizer.from_pretrained("unsloth/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit")
+
+# Load the fine-tuned model
+fine_tuned_model = AutoModelForCausalLM.from_pretrained("kas1/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit-John1")
+fine_tuned_tokenizer = AutoTokenizer.from_pretrained("kas1/DeepSeek-R1-Distill-Llama-8B-unsloth-bnb-4bit-John1")
+
+# Function to generate responses from both models
+def compare_models(prompt):
+    # Generate response from the original model
+    original_inputs = original_tokenizer(prompt, return_tensors="pt")
+    original_outputs = original_model.generate(**original_inputs)
+    original_response = original_tokenizer.decode(original_outputs[0], skip_special_tokens=True)
+
+    # Generate response from the fine-tuned model
+    fine_tuned_inputs = fine_tuned_tokenizer(prompt, return_tensors="pt")
+    fine_tuned_outputs = fine_tuned_model.generate(**fine_tuned_inputs)
+    fine_tuned_response = fine_tuned_tokenizer.decode(fine_tuned_outputs[0], skip_special_tokens=True)
+
+    return original_response, fine_tuned_response
+
+# Function to handle batch testing with a JSON file
+def batch_test(json_file):
+    import json
+    results = []
+    data = json.load(json_file)
+
+    for item in data:
+        question = item.get("question", "")
+        expected_answer = item.get("answer", "")
+
+        # Generate responses from both models
+        original_response, fine_tuned_response = compare_models(question)
+
+        results.append({
+            "question": question,
+            "expected_answer": expected_answer,
+            "original_model_response": original_response,
+            "fine_tuned_model_response": fine_tuned_response
+        })
+
+    return results
+
+# Define the Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Compare Two Models Side by Side")
+    
+    # Single prompt comparison
+    with gr.Tab("Single Prompt"):
+        prompt_input = gr.Textbox(label="Enter a Prompt/Question")
+        compare_button = gr.Button("Compare Responses")
+        original_output = gr.Textbox(label="Original Model Response")
+        fine_tuned_output = gr.Textbox(label="Fine-Tuned Model Response")
+
+        compare_button.click(
+            compare_models,
+            inputs=prompt_input,
+            outputs=[original_output, fine_tuned_output]
+        )
+
+    # Batch testing with a JSON file
+    with gr.Tab("Batch Testing"):
+        json_file_input = gr.File(label="Upload JSON File with Questions")
+        batch_results = gr.JSON(label="Comparison Results")
+        batch_button = gr.Button("Run Batch Test")
+
+        batch_button.click(
+            batch_test,
+            inputs=json_file_input,
+            outputs=batch_results
+        )
+
+# Launch the app
+demo.launch()

requirements.txt

@@ -0,0 +1,2 @@
+transformers
+gradio