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b9eac6c a1bfead b9eac6c a1bfead b9eac6c a1bfead b9eac6c a1bfead b9eac6c c0105ab b9eac6c e7807af b9eac6c a1bfead b9eac6c a1bfead | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 | import gradio as gr
import requests, json, os, time
from fastapi import FastAPI, Request
from fastapi.responses import JSONResponse
import uvicorn
OLLAMA = "http://localhost:11434"
MODEL = "FableForge-AI/shellwhisperer"
fastapi_app = FastAPI(docs_url=None, redoc_url=None)
@fastapi_app.post("/api/chat")
async def api_chat(request: Request):
body = await request.json()
try:
r = requests.post(f"{OLLAMA}/api/chat", json=body, timeout=120)
return JSONResponse(content=r.json())
except Exception as e:
return JSONResponse(status_code=500, content={"error": str(e)})
# ββ helpers ββ
def model_info():
"""Get model details and status."""
info = {
"ready": False,
"model": MODEL,
"size": "?",
"speed": "?",
"error": "",
}
try:
r = requests.get(f"{OLLAMA}/api/tags", timeout=5)
if r.status_code == 200:
models = r.json().get("models", [])
for m in models:
if MODEL in m.get("name", ""):
info["ready"] = True
size_gb = m.get("size", 0) / 1e9
info["size"] = f"{size_gb:.1f} GB"
break
if not info["ready"]:
info["error"] = "Model not pulled yet"
else:
info["error"] = f"Ollama returned {r.status_code}"
except requests.ConnectionError:
info["error"] = "Ollama not running"
except Exception as e:
info["error"] = str(e)
return info
def generate(prompt, temp):
if not prompt.strip():
yield "β οΈ Enter a prompt first."
return
info = model_info()
if not info["ready"]:
yield f"β³ Model loading... ({info['error']})"
return
try:
r = requests.post(f"{OLLAMA}/api/chat", json={
"model": MODEL,
"messages": [
{"role": "system", "content": "You are ShellWhisperer-1.5B, a shell and CLI specialist. Output working code only, no explanations."},
{"role": "user", "content": prompt}
],
"stream": True,
"options": {"temperature": temp, "num_ctx": 16384}
}, stream=True, timeout=120)
full = []
for line in r.iter_lines():
if not line:
continue
try:
d = json.loads(line)
chunk = d.get("message", {}).get("content", "")
if chunk:
full.append(chunk)
yield "".join(full)
except json.JSONDecodeError:
pass
if not full:
yield "β οΈ Empty response from model. Try again."
except requests.Timeout:
yield "β° Request timed out after 120s. Try a shorter prompt."
except Exception as e:
yield f"β Error: {e}"
# ββ UI ββ
with gr.Blocks(
title="ShellWhisperer-1.5B Β· API",
theme=gr.themes.Soft(),
fill_height=True,
css="""footer { display: none !important; }
.status-ok { color: #22c55e; font-weight: 600; }
.status-loading { color: #f59e0b; font-weight: 600; }
.status-err { color: #ef4444; font-weight: 600; }
.api-box { background: #1f2937; color: #e5e7eb; padding: 1em; border-radius: 8px; font-family: monospace; font-size: 0.9em; overflow-x: auto; }
""",
) as demo:
gr.Markdown("""# π ShellWhisperer-1.5B Β· API Demo
**CLI & Shell Code Specialist** Β· 1 GB Β· 29 tok/s Β· 16K context Β· Apache 2.0
Built by **FableForge AI** β part of the [Mythos model ecosystem](https://github.com/KingLabsA/mythos).
""")
# ββ status dashboard ββ
with gr.Row():
status_badge = gr.Markdown("β³ Checking...")
model_size = gr.Markdown("")
model_speed = gr.Markdown("")
with gr.Tabs():
with gr.TabItem("π§ͺ Try it"):
with gr.Row():
with gr.Column(scale=3):
inp = gr.Textbox(
label="Prompt",
placeholder="Write a bash script to...",
lines=4,
)
with gr.Row():
temp = gr.Slider(0.0, 1.0, value=0.3, step=0.05, label="Temperature")
btn = gr.Button("π Generate", variant="primary", scale=1, size="lg")
out = gr.Textbox(label="Output", lines=16)
gr.Markdown("### π‘ Try these")
gr.Examples(
examples=[
["Write a bash script to monitor a directory for new files and log them"],
["Write a Python script to batch resize images to 800px wide"],
["Write a Docker Compose file for a web app with PostgreSQL"],
["Write a git pre-commit hook that runs tests"],
],
inputs=inp,
label="",
)
with gr.TabItem("π‘ API"):
gr.Markdown("""### REST API
This space exposes a standard Ollama-compatible chat endpoint.
```
POST /api/chat
Content-Type: application/json
```
**cURL:**
```bash
curl -X POST https://karma-devops-shellwhisperer-demo.hf.space/api/chat \\
-H "Content-Type: application/json" \\
-d '{
"model": "FableForge-AI/shellwhisperer",
"messages": [{"role": "user", "content": "Write a bash script"}],
"stream": false,
"options": {"temperature": 0.3, "num_ctx": 16384}
}'
```
**Python:**
```python
import requests
r = requests.post("https://karma-devops-shellwhisperer-demo.hf.space/api/chat", json={
"model": "FableForge-AI/shellwhisperer",
"messages": [{"role": "user", "content": "Write a bash script"}],
"stream": False,
"options": {"temperature": 0.3, "num_ctx": 16384}
})
print(r.json()["message"]["content"])
```
""")
with gr.TabItem("βΉοΈ About"):
gr.Markdown("""### Model
| Property | Value |
|---|---|
| Name | ShellWhisperer-1.5B |
| Author | FableForge AI ([KingLabsA](https://github.com/KingLabsA)) |
| Size | 1 GB |
| Speed | ~29 tok/s (T4 GPU) |
| Context | 16,384 tokens |
| License | Apache 2.0 |
| Base | Qwen2.5-Coder-1.5B-Instruct |
### Links
- [GitHub: KingLabsA/mythos](https://github.com/KingLabsA/mythos)
- [HuggingFace: King3Djbl](https://huggingface.co/King3Djbl)
- [HuggingFace: fableforge-ai](https://huggingface.co/fableforge-ai)
- [Ollama: FableForge-AI](https://ollama.com/FableForge-AI)
""")
# ββ events ββ
def refresh_status():
info = model_info()
if info["ready"]:
badge = f'<span class="status-ok">β
Model: Ready</span>'
size = f'<span class="status-ok">π¦ {info["size"]}</span>'
speed = '<span class="status-ok">β‘ ~29 tok/s</span>'
else:
badge = f'<span class="status-loading">β³ Model: {info["error"]}</span>'
size = ""
speed = ""
return badge, size, speed
demo.load(fn=refresh_status, outputs=[status_badge, model_size, model_speed])
gr.Timer(30).tick(fn=refresh_status, outputs=[status_badge, model_size, model_speed])
btn.click(
fn=generate,
inputs=[inp, temp],
outputs=out,
)
app = gr.mount_gradio_app(fastapi_app, demo, path="/")
if __name__ == "__main__":
uvicorn.run(app, host="0.0.0.0", port=7860)
|