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README.md

@@ -1,116 +1,18 @@
 ---
-pretty_name: HyperScholar-OmniPython (50K Spec→Code→Tests) — WithIn Us AI
-language:
-- en
-license: apache-2.0
-task_categories:
-- text-generation
-- text2text-generation
-tags:
-- python
-- code
-- instruction-tuning
-- sft
-- unit-tests
-- software-engineering
-- security
-- async
-- algorithms
-- trl
-size_categories:
-- 10K<n<100K
-configs:
-- config_name: codeonly
-  data_files: HyperScholar-OmniPython-50K-CodeOnly.jsonl
-- config_name: hyperreason
-  data_files: HyperScholar-OmniPython-50K-HyperReason.jsonl
-- config_name: autotrain_chat
-  data_files: HyperScholar-OmniPython-50K-AutoTrain-Chat.jsonl
+title: Gemma Code Python — WithIn Us AI
+emoji: 🧠
+colorFrom: indigo
+colorTo: purple
+sdk: gradio
+sdk_version: "4.44.1"
+app_file: app.py
+pinned: false
 ---
 
-# HyperScholar-OmniPython (50K) — WithIn Us AI
+# Gemma Code Python — WithIn Us AI
 
-Publisher: **WithIn Us AI**  
-Hugging Face: **gss1147**
+This Space fixes the Hugging Face Spaces configuration error by specifying `sdk: gradio`.
 
-A developer-focused Python fine-tuning dataset that trains **engineering discipline** with a Mixture-of-Experts flavor:
-clear requirements, edge cases, complexity awareness, secure defaults, and minimal tests.
-
-## What’s included
-
-Two configs (each **50,000** records):
-
-- **codeonly**: assistant outputs *final code only* (clean production behavior)
-- **hyperreason**: assistant outputs:
-  - Expert (MoE label: ALG | DATA | ASYNC | SEC | API | TEST | PERF | STYLE)
-  - Spec
-  - Edge cases
-  - Complexity
-  - Implementation
-  - Minimal tests
-
-## Why developers want this
-
-- Teaches habits that improve reliability in real codebases:
-  - fail-closed validation and security patterns
-  - minimal tests and typing discipline
-  - async/reliability primitives (timeouts, concurrency limits, backoff, circuit breaker)
-  - standard library competence and clean APIs
-
-## Files in this repo
-
-- `HyperScholar-OmniPython-50K-CodeOnly.jsonl`
-- `HyperScholar-OmniPython-50K-HyperReason.jsonl`
-- `train_sft_omni_50k.py`
-- `requirements_omni_50k.txt`
-- `hf_upload_dataset.py`
-- `EVAL_README.md`
-- `LEADERBOARD.md`
-- `MODEL_SUBMISSION_TEMPLATE.md`
-
-## Schema (JSONL)
-
-Each line:
-```json
-{
-  "id": "OMNI00001",
-  "tags": ["..."],
-  "prompt": [
-    {"role":"system","content":"..."},
-    {"role":"user","content":"..."}
-  ],
-  "completion": [
-    {"role":"assistant","content":"..."}
-  ]
-}
-```
-
-## Train (TRL SFTTrainer)
-
-```bash
-pip install -r requirements_omni_50k.txt
-
-python train_sft_omni_50k.py \
-  --model YOUR_BASE_MODEL \
-  --dataset HyperScholar-OmniPython-50K-CodeOnly.jsonl \
-  --output_dir out_omni_50k \
-  --use_lora --use_4bit --bf16 \
-  --max_seq_len 4096 \
-  --gradient_accumulation_steps 16 \
-  --learning_rate 2e-4 \
-  --num_train_epochs 1 \
-  --packing
-```
-
-To train the structured “engineering discipline” behavior:
-- set `--dataset HyperScholar-OmniPython-50K-HyperReason.jsonl`
-
-## Contribute / publish your fine-tuned model
-
-Train a model on this dataset and publish it on Hugging Face, then add your result in `LEADERBOARD.md`
-using `MODEL_SUBMISSION_TEMPLATE.md`.
-
-
-## AutoTrain (no-code) ready
-
-Use config `autotrain_chat` in AutoTrain Advanced (LLM → SFT). Column mapping: `{ "text": "text" }` and set `chat_template=tokenizer`.
+## Next
+Connect your fine-tuned CodeGemma model trained on:
+- `gss1147/HyperScholar-OmniPython-50K` (HyperReason recommended)

WithInUsAI_Gemma_Code_Python_Space_Fix_1767207633.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a6a66091f74f5d814f0ca49a967032770208c305367e0ea76dd80bcef3ade87c
+size 1132

app.py

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+import gradio as gr
+
+TITLE = "WithIn Us AI — Gemma Code Python"
+DESC = (
+    "Space is running. Next: connect your fine-tuned CodeGemma model trained on "
+    "gss1147/HyperScholar-OmniPython-50K (HyperReason)."
+)
+
+def respond(prompt: str) -> str:
+    prompt = (prompt or "").strip()
+    if not prompt:
+        return "Enter a prompt to test the Space. (Model hookup comes next.)"
+    return (
+        "Space OK (Gradio running).\n\n"
+        "Next steps:\n"
+        "1) Train CodeGemma with AutoTrain (SFT)\n"
+        "2) Publish model under gss1147/WithInUsAI-...\n"
+        "3) Update this Space to load the model\n\n"
+        f"Your prompt:\n{prompt}"
+    )
+
+demo = gr.Interface(
+    fn=respond,
+    inputs=gr.Textbox(lines=10, label="Prompt"),
+    outputs=gr.Textbox(lines=14, label="Response"),
+    title=TITLE,
+    description=DESC,
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio==4.44.1