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Valtry commited on Aug 8, 2025

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67c9c6c

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1 Parent(s): 6709abd

Update app.py

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Files changed (1) hide show

app.py +40 -57

app.py CHANGED Viewed

@@ -1,45 +1,41 @@
 import os
 import time
 import uuid
-from io import BytesIO
 from typing import Optional
-from fastapi import FastAPI, Request
 from fastapi.responses import HTMLResponse, JSONResponse
 from fastapi.staticfiles import StaticFiles
 from pydantic import BaseModel
-# Put the HF / transformers cache into a writable folder
 os.environ["HF_HOME"] = "/app/cache"
 os.environ["TRANSFORMERS_CACHE"] = "/app/cache"
 os.makedirs("/app/cache", exist_ok=True)
 os.makedirs("/app/static", exist_ok=True)
-# Import after setting env
 import torch
 from diffusers import StableDiffusionPipeline
 # -------- CONFIG --------
-MODEL_ID = "runwayml/stable-diffusion-v1-5"   # change if you prefer another model
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 STATIC_FOLDER = "/app/static"
 # ------------------------
 app = FastAPI(title="Valtry Text→Image API")
-# Mount static folder so generated images are publicly accessible at /static/...
 app.mount("/static", StaticFiles(directory=STATIC_FOLDER), name="static")
-# Load model once at startup
-print("Loading model", MODEL_ID, "to device", DEVICE)
-# If your model is gated, set use_auth_token=os.getenv("HF_TOKEN") in from_pretrained(...)
 pipe = StableDiffusionPipeline.from_pretrained(
     MODEL_ID,
     torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32,
 )
 pipe = pipe.to(DEVICE)
-pipe.safety_checker = pipe.safety_checker if hasattr(pipe, "safety_checker") else None
-print("Model loaded")
 class GenerateReq(BaseModel):
     prompt: str
@@ -49,13 +45,12 @@ class GenerateReq(BaseModel):
 @app.post("/generate")
 async def generate(req: GenerateReq):
-    if not req.prompt or not req.prompt.strip():
         return JSONResponse({"error": "prompt is required"}, status_code=400)
     seed = req.seed if req.seed is not None else int(time.time() * 1000) % 2**32
     generator = torch.Generator(device=DEVICE).manual_seed(seed) if DEVICE == "cuda" else None
-    # Generate image
     try:
         result = pipe(
             req.prompt,
@@ -64,89 +59,77 @@ async def generate(req: GenerateReq):
             generator=generator,
         )
     except Exception as e:
-        return JSONResponse({"error": f"model generation failed: {str(e)}"}, status_code=500)
     image = result.images[0]
-    # Save image file
     filename = f"img_{int(time.time())}_{uuid.uuid4().hex[:8]}.png"
     file_path = os.path.join(STATIC_FOLDER, filename)
     image.save(file_path)
-    # Return the public URL (relative)
-    return {"url": f"/static/{filename}", "filename": filename}
-# Simple home page for quick testing in browser
 @app.get("/", response_class=HTMLResponse)
 async def home():
-    return """
     <!doctype html>
     <html>
     <head>
       <meta charset="utf-8"/>
-      <title>Valtry Text→Image</title>
       <style>
-        body{font-family:Arial,sans-serif;margin:32px;background:#f7f7f7}
-        input, button, textarea{font-size:16px;padding:10px;width:100%;box-sizing:border-box;margin-top:8px}
-        #result{margin-top:20px}
-        img{max-width:100%;border:1px solid #ccc;padding:6px;background:#fff}
-        label{font-weight:600}
       </style>
     </head>
     <body>
       <h2>Valtry — Text → Image</h2>
-      <label>Prompt</label>
-      <textarea id="prompt" rows="3" placeholder="A fantasy castle on a cliff at sunset"></textarea>
-      <label>Max steps (num_inference_steps)</label>
       <input id="steps" type="number" value="25" min="1" max="150"/>
-      <label>Guidance scale</label>
       <input id="scale" type="number" value="7.5" step="0.1" min="1" max="20"/>
-      <label>Seed (optional)</label>
-      <input id="seed" type="number" placeholder="leave empty for random"/>
       <button onclick="generate()">Generate Image</button>
       <div id="status"></div>
       <div id="result"></div>
       <script>
-        async function generate(){
           const prompt = document.getElementById('prompt').value;
-          const steps = parseInt(document.getElementById('steps').value || 25);
-          const scale = parseFloat(document.getElementById('scale').value || 7.5);
           const seedVal = document.getElementById('seed').value;
-          document.getElementById('status').textContent = "⏳ Generating — this may take a bit...";
           document.getElementById('result').innerHTML = "";
-          const body = {
-            prompt: prompt,
-            num_inference_steps: steps,
-            guidance_scale: scale
-          };
           if (seedVal) body.seed = parseInt(seedVal);
-          try {
-            const res = await fetch('/generate', {
               method: 'POST',
-              headers: {'Content-Type': 'application/json'},
               body: JSON.stringify(body)
-            });
-            if (!res.ok) {
-              const txt = await res.text();
-              document.getElementById('status').textContent = '❌ Error ' + res.status + ': ' + txt;
-              return;
-            }
             const data = await res.json();
-            document.getElementById('status').textContent = '✅ Done — image below';
-            document.getElementById('result').innerHTML = `<img src="${data.url}" alt="generated-image"/>`;
-          } catch (err) {
-            document.getElementById('status').textContent = '❌ Exception: ' + err.message;
-          }
-        }
       </script>
     </body>
     </html>
     """
-# optional health route
 @app.get("/health")
 async def health():
     return {"status": "ok"}

 import os
 import time
 import uuid
 from typing import Optional
+from fastapi import FastAPI
 from fastapi.responses import HTMLResponse, JSONResponse
 from fastapi.staticfiles import StaticFiles
 from pydantic import BaseModel
+# Set cache directories to writable paths
 os.environ["HF_HOME"] = "/app/cache"
 os.environ["TRANSFORMERS_CACHE"] = "/app/cache"
 os.makedirs("/app/cache", exist_ok=True)
 os.makedirs("/app/static", exist_ok=True)
 import torch
 from diffusers import StableDiffusionPipeline
 # -------- CONFIG --------
+MODEL_ID = "runwayml/stable-diffusion-v1-5"
 DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
 STATIC_FOLDER = "/app/static"
+SPACE_URL = "https://valtry-my-image.hf.space"  # <-- CHANGE THIS to your Space's URL
 # ------------------------
 app = FastAPI(title="Valtry Text→Image API")
+# Serve static folder
 app.mount("/static", StaticFiles(directory=STATIC_FOLDER), name="static")
+print(f"Loading model {MODEL_ID} on {DEVICE}...")
 pipe = StableDiffusionPipeline.from_pretrained(
     MODEL_ID,
     torch_dtype=torch.float16 if DEVICE == "cuda" else torch.float32,
 )
 pipe = pipe.to(DEVICE)
+pipe.safety_checker = getattr(pipe, "safety_checker", None)
+print("✅ Model loaded")
 class GenerateReq(BaseModel):
     prompt: str
 @app.post("/generate")
 async def generate(req: GenerateReq):
+    if not req.prompt.strip():
         return JSONResponse({"error": "prompt is required"}, status_code=400)
     seed = req.seed if req.seed is not None else int(time.time() * 1000) % 2**32
     generator = torch.Generator(device=DEVICE).manual_seed(seed) if DEVICE == "cuda" else None
     try:
         result = pipe(
             req.prompt,
             generator=generator,
         )
     except Exception as e:
+        return JSONResponse({"error": f"generation failed: {str(e)}"}, status_code=500)
     image = result.images[0]
     filename = f"img_{int(time.time())}_{uuid.uuid4().hex[:8]}.png"
     file_path = os.path.join(STATIC_FOLDER, filename)
     image.save(file_path)
+    # ✅ Full public URL so it loads in browser
+    public_url = f"{SPACE_URL}/static/{filename}"
+    return {"url": public_url, "filename": filename}
 @app.get("/", response_class=HTMLResponse)
 async def home():
+    return f"""
     <!doctype html>
     <html>
     <head>
       <meta charset="utf-8"/>
+      <title>Valtry — Text→Image</title>
       <style>
+        body{{font-family:Arial,sans-serif;margin:32px;background:#f7f7f7}}
+        input, button, textarea{{font-size:16px;padding:10px;width:100%;margin-top:8px}}
+        img{{max-width:100%;border:1px solid #ccc;padding:6px;background:#fff;margin-top:20px}}
       </style>
     </head>
     <body>
       <h2>Valtry — Text → Image</h2>
+      <textarea id="prompt" rows="3" placeholder="A fantasy castle on a cliff at sunset"></textarea><br>
       <input id="steps" type="number" value="25" min="1" max="150"/>
       <input id="scale" type="number" value="7.5" step="0.1" min="1" max="20"/>
+      <input id="seed" type="number" placeholder="optional seed"/>
       <button onclick="generate()">Generate Image</button>
       <div id="status"></div>
       <div id="result"></div>
       <script>
+        async function generate(){{
           const prompt = document.getElementById('prompt').value;
+          const steps = parseInt(document.getElementById('steps').value);
+          const scale = parseFloat(document.getElementById('scale').value);
           const seedVal = document.getElementById('seed').value;
+          document.getElementById('status').textContent = "⏳ Generating...";
           document.getElementById('result').innerHTML = "";
+          const body = {{ prompt, num_inference_steps: steps, guidance_scale: scale }};
           if (seedVal) body.seed = parseInt(seedVal);
+          try {{
+            const res = await fetch('/generate', {{
               method: 'POST',
+              headers: {{ 'Content-Type': 'application/json' }},
               body: JSON.stringify(body)
+            }});
             const data = await res.json();
+            if (res.ok) {{
+              document.getElementById('status').textContent = "✅ Done";
+              document.getElementById('result').innerHTML = `<img src="${data.url}" alt="Generated image"/>`;
+            }} else {{
+              document.getElementById('status').textContent = "❌ Error: " + data.error;
+            }}
+          }} catch (err) {{
+            document.getElementById('status').textContent = "❌ " + err.message;
+          }}
+        }}
       </script>
     </body>
     </html>
     """
 @app.get("/health")
 async def health():
     return {"status": "ok"}