Update app.py

app.py

@@ -1,154 +1,89 @@
 import gradio as gr
+import torch
 import numpy as np
-import random
 
-# import spaces #[uncomment to use ZeroGPU]
-from diffusers import DiffusionPipeline
-import torch
+from sentence_transformers import SentenceTransformer, util
 
+# 1. Load your fine-tuned retrieval model (on CodeSearchNet - Python)
+#    This is the model you pushed to the Hugging Face Hub after training.
+model_name = "juanwisz/modernbert-python-code-retrieval"
 device = "cuda" if torch.cuda.is_available() else "cpu"
-model_repo_id = "stabilityai/sdxl-turbo"  # Replace to the model you would like to use
-
-if torch.cuda.is_available():
-    torch_dtype = torch.float16
-else:
-    torch_dtype = torch.float32
-
-pipe = DiffusionPipeline.from_pretrained(model_repo_id, torch_dtype=torch_dtype)
-pipe = pipe.to(device)
-
-MAX_SEED = np.iinfo(np.int32).max
-MAX_IMAGE_SIZE = 1024
-
-
-# @spaces.GPU #[uncomment to use ZeroGPU]
-def infer(
-    prompt,
-    negative_prompt,
-    seed,
-    randomize_seed,
-    width,
-    height,
-    guidance_scale,
-    num_inference_steps,
-    progress=gr.Progress(track_tqdm=True),
-):
-    if randomize_seed:
-        seed = random.randint(0, MAX_SEED)
-
-    generator = torch.Generator().manual_seed(seed)
-
-    image = pipe(
-        prompt=prompt,
-        negative_prompt=negative_prompt,
-        guidance_scale=guidance_scale,
-        num_inference_steps=num_inference_steps,
-        width=width,
-        height=height,
-        generator=generator,
-    ).images[0]
-
-    return image, seed
-
-
-examples = [
-    "Astronaut in a jungle, cold color palette, muted colors, detailed, 8k",
-    "An astronaut riding a green horse",
-    "A delicious ceviche cheesecake slice",
-]
 
+# SentenceTransformer automatically handles tokenizer + embedding
+embedding_model = SentenceTransformer(model_name, device=device)
+
+# 2. Define a function to:
+#    - Parse code snippets from the text box (split by "---")
+#    - Compute embeddings for the user’s query and each snippet
+#    - Return the top 3 most relevant code snippets based on cosine similarity
+def retrieve_top_snippets(query, code_input):
+    # Split the code snippets by "---"
+    # Each snippet is trimmed for cleanliness
+    snippets = [s.strip() for s in code_input.split("---") if s.strip()]
+
+    # Edge-case: if user provided no code, just return
+    if len(snippets) == 0:
+        return "No code snippets detected (make sure to separate them with ---)."
+
+    # Embed the query and code snippets
+    query_emb = embedding_model.encode(query, convert_to_tensor=True)
+    snippets_emb = embedding_model.encode(snippets, convert_to_tensor=True)
+
+    # Compute cosine similarities [batch_size x 1] with all code snippets
+    cos_scores = util.cos_sim(query_emb, snippets_emb)[0]
+
+    # Sort results by decreasing score
+    # argsort(descending) means the first indices are the most relevant
+    top_indices = torch.topk(cos_scores, k=min(3, len(snippets))).indices
+
+    # Prepare text output with top 3 matches
+    results = []
+    for idx in top_indices:
+        score = cos_scores[idx].item()
+        snippet_text = snippets[idx]
+        results.append(f"**Score**: {score:.4f}\n```python\n{snippet_text}\n```")
+
+    # Join all results nicely
+    return "\n\n".join(results)
+
+
+#####################
+### Gradio Layout ###
+#####################
 css = """
-#col-container {
+#container {
     margin: 0 auto;
-    max-width: 640px;
+    max-width: 700px;
 }
 """
 
 with gr.Blocks(css=css) as demo:
-    with gr.Column(elem_id="col-container"):
-        gr.Markdown(" # Text-to-Image Gradio Template")
+    gr.Markdown("# Code Retrieval using ModernBERT\n"
+                "Enter a natural language query and paste multiple Python code snippets, "
+                "delimited by `---`. We'll return the top 3 matches.")
 
+    with gr.Column(elem_id="container"):
         with gr.Row():
-            prompt = gr.Text(
-                label="Prompt",
-                show_label=False,
-                max_lines=1,
-                placeholder="Enter your prompt",
-                container=False,
-            )
-
-            run_button = gr.Button("Run", scale=0, variant="primary")
-
-        result = gr.Image(label="Result", show_label=False)
-
-        with gr.Accordion("Advanced Settings", open=False):
-            negative_prompt = gr.Text(
-                label="Negative prompt",
-                max_lines=1,
-                placeholder="Enter a negative prompt",
-                visible=False,
-            )
-
-            seed = gr.Slider(
-                label="Seed",
-                minimum=0,
-                maximum=MAX_SEED,
-                step=1,
-                value=0,
+            query_input = gr.Textbox(
+                label="Natural Language Query",
+                placeholder="What does your function do? e.g., 'Parse JSON from a string'"
             )
 
-            randomize_seed = gr.Checkbox(label="Randomize seed", value=True)
-
-            with gr.Row():
-                width = gr.Slider(
-                    label="Width",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-                height = gr.Slider(
-                    label="Height",
-                    minimum=256,
-                    maximum=MAX_IMAGE_SIZE,
-                    step=32,
-                    value=1024,  # Replace with defaults that work for your model
-                )
-
-            with gr.Row():
-                guidance_scale = gr.Slider(
-                    label="Guidance scale",
-                    minimum=0.0,
-                    maximum=10.0,
-                    step=0.1,
-                    value=0.0,  # Replace with defaults that work for your model
-                )
-
-                num_inference_steps = gr.Slider(
-                    label="Number of inference steps",
-                    minimum=1,
-                    maximum=50,
-                    step=1,
-                    value=2,  # Replace with defaults that work for your model
-                )
-
-        gr.Examples(examples=examples, inputs=[prompt])
-    gr.on(
-        triggers=[run_button.click, prompt.submit],
-        fn=infer,
-        inputs=[
-            prompt,
-            negative_prompt,
-            seed,
-            randomize_seed,
-            width,
-            height,
-            guidance_scale,
-            num_inference_steps,
-        ],
-        outputs=[result, seed],
-    )
+        code_snippets_input = gr.Textbox(
+            label="Paste Python functions (delimited by ---)",
+            lines=10,
+            placeholder="Example:\n---\ndef parse_json(data):\n    return json.loads(data)\n---\ndef add_numbers(a, b):\n    return a + b\n---"
+        )
+
+        search_btn = gr.Button("Search", variant="primary")
+        results_output = gr.Markdown(label="Top 3 Matches")
+
+        # On click, run our retrieval function
+        search_btn.click(
+            fn=retrieve_top_snippets,
+            inputs=[query_input, code_snippets_input],
+            outputs=results_output
+        )
 
 if __name__ == "__main__":
     demo.launch()