Create app.py

app.py

@@ -0,0 +1,59 @@
+
+import gradio as gr
+
+from gradientai import Gradient
+import os
+import pandas as pd
+
+os.environ['GRADIENT_WORKSPACE_ID']='9d0447f2-fcd4-4177-9145-9f019fd59f1e_workspace'
+os.environ['GRADIENT_ACCESS_TOKEN']='cPErsUMgadGMbzeq8z8W36eJn7UA0Uob'
+
+df = pd.read_csv("https://raw.githubusercontent.com/CS-5302/CS-5302-Project-Group-15/main/Datasets/testing/combined_df.csv")
+df
+
+BATCH_SIZE = 100
+NUM_EPOCHS = 1
+
+def create_model_adapter(gradient):
+    base_model = gradient.get_base_model(base_model_slug="nous-hermes2")
+    new_model_adapter = base_model.create_model_adapter(
+        name="meta/llama-2-7b:73001d654114dad81ec65da3b834e2f691af1e1526453189b7bf36fb3f32d0f9"
+    )
+    print(f"Created model adapter with id {new_model_adapter.id}")
+    return new_model_adapter
+
+def fine_tune_in_batches(df, gradient, batch_size, num_epochs):
+    new_model_adapter = create_model_adapter(gradient)
+
+    # Split the DataFrame into batches
+    batches = [df[i:i + batch_size] for i in range(0, len(df), batch_size)]
+
+    # Iterate over batches and perform fine-tuning
+    for batch_index, batch in enumerate(batches):
+        fine_tuning_samples = []
+        for _, row in batch.iterrows():
+            fine_tuning_samples.append({
+                "inputs": f"### Instruction: {row['prompts']}",
+                "targets": f"### Response: {row['results']}"
+            })
+
+        # Fine-tune for the given number of epochs
+        for epoch in range(num_epochs):
+            print(f"Fine-tuning batch {batch_index + 1} (epoch {epoch + 1})")
+            new_model_adapter.fine_tune(samples=fine_tuning_samples)
+
+    return new_model_adapter
+
+
+def predict(prompt):
+    gradient = Gradient()
+    model_adapter = fine_tune_in_batches(df, gradient, BATCH_SIZE, NUM_EPOCHS)
+    sample_query = f"### Instruction: {prompt} \n\n### Response:"
+    completion = model_adapter.complete(query=sample_query, max_generated_token_count=100).generated_output
+    model_adapter.delete()
+    gradient.close()
+    return completion
+
+interface = gr.Interface(fn=predict, inputs="text", outputs="text")
+
+interface.launch()