Updated app.py _pycache

app.py

@@ -6,15 +6,17 @@ from live_preview_helpers import (
     flux_pipe_call_that_returns_an_iterable_of_images as flux_iter,
 )
 
-# Device selection
-device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+# Device and dtype selection
+USE_CUDA = torch.cuda.is_available()
+DTYPE = torch.float16 if USE_CUDA else torch.float32
+device = torch.device("cuda" if USE_CUDA else "cpu")
 
 # PUBLIC Stable Diffusion pipeline setup
 pipe = StableDiffusionPipeline.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
+    "runwayml/stable-diffusion-v1-5", torch_dtype=DTYPE
 ).to(device)
 good_vae = AutoencoderKL.from_pretrained(
-    "runwayml/stable-diffusion-v1-5", subfolder="vae", torch_dtype=torch.float16
+    "runwayml/stable-diffusion-v1-5", subfolder="vae", torch_dtype=DTYPE
 ).to(device)
 pipe.flux_pipe_call_that_returns_an_iterable_of_images = flux_iter.__get__(pipe)
 
@@ -23,6 +25,7 @@ LLM_SPACES = [
     "https://huggingfaceh4-zephyr-chat.hf.space",
     "huggingface-projects/gemma-2-9b-it",
 ]
+
 def first_live_space(space_ids):
     for sid in space_ids:
         try:
@@ -33,22 +36,29 @@ def first_live_space(space_ids):
             return c
         except Exception as e:
             print(f"[warn] {sid} unusable → {e}")
-    raise RuntimeError("No live chat Space found!")
+    print("[warn] No live chat Space found; falling back to local responses.")
+    return None
+
 llm_client = first_live_space(LLM_SPACES)
 CHAT_API = "/chat"
+
 def call_llm(prompt, max_tokens=256, temperature=0.6, top_p=0.9):
-    try:
-        return llm_client.predict(
-            prompt, max_tokens, temperature, top_p, api_name=CHAT_API
-        ).strip()
-    except Exception as exc:
-        print(f"[error] LLM failure → {exc}")
-        return "…"
+    if llm_client is not None:
+        try:
+            return llm_client.predict(
+                prompt, max_tokens, temperature, top_p, api_name=CHAT_API
+            ).strip()
+        except Exception as exc:
+            print(f"[error] LLM failure → {exc}")
+    # Local fallback if no LLM API available
+    print("[warn] Returning local fallback response.")
+    return "No LLM API available. Please enter your own text."
 
 # Datasets and prompt templates
 ds = load_dataset("MohamedRashad/FinePersonas-Lite", split="train")
 def random_persona():
     return ds[random.randint(0, len(ds) - 1)]["persona"]
+
 WORLD_PROMPT = (
     "Invent a short, unique and vivid world description. Respond with the description only."
 )
@@ -73,17 +83,10 @@ def generate_character(world_desc, persona_desc, progress=gr.Progress(track_tqdm
     )
     try:
         return json.loads(raw)
-    except json.JSONDecodeError:
-        raw = call_llm(
-            PROMPT_TEMPLATE.format(
-                persona_description=persona_desc,
-                world_description=world_desc,
-            ),
-            max_tokens=1024,
-        )
-        return json.loads(raw)
+    except Exception:
+        # Fallback for user input/manual override
+        return {"name": "Unnamed", "appearance": "Manual entry required."}
 
-# Chaining (connected characters)
 CHAIN_PROMPT_TEMPLATE = """
 You are crafting an interconnected character ensemble for a shared world.
 
@@ -122,15 +125,11 @@ def generate_connected_characters(world_desc, persona_desc, progress=gr.Progress
     )
     try:
         return json.loads(raw)
-    except json.JSONDecodeError:
-        raw = call_llm(
-            CHAIN_PROMPT_TEMPLATE.format(
-                world_description=world_desc,
-                primary_persona=persona_desc
-            ),
-            max_tokens=2048
-        )
-        return json.loads(raw)
+    except Exception:
+        # Fallback for user input/manual override
+        return [{"name": "Unnamed Protagonist", "role": "protagonist"},
+                {"name": "Unnamed Ally", "role": "ally"},
+                {"name": "Unnamed Nemesis", "role": "nemesis"}]
 
 # Gradio UI
 DESCRIPTION = """
@@ -161,18 +160,22 @@ with gr.Blocks(title="Connected Character Chain Generator", theme="Nymbo/Nymbo_T
         json_out = gr.JSON(label="Character Description")
     chained_out = gr.JSON(label="Connected Characters (Protagonist, Ally, Nemesis)")
 
-    jls_extract_var = lambda character: next(pipe.flux_pipe_call_that_returns_an_iterable_of_images(
-                prompt=character["appearance"],
-                guidance_scale=7.5,
-                num_inference_steps=25,
-                width=512,
-                height=512,
-                output_type="pil"
-            ))
+    def sd_image_from_character(character):
+        # Use appearance or fallback if needed
+        prompt = character.get("appearance", "A unique portrait, digital art, fantasy character, 4k")
+        return next(pipe.flux_pipe_call_that_returns_an_iterable_of_images(
+            prompt=prompt,
+            guidance_scale=7.5,
+            num_inference_steps=25,
+            width=512,
+            height=512,
+            output_type="pil"
+        ))
+
     btn_generate.click(
         generate_character, [world_tb, persona_tb], [json_out]
     ).then(
-        jls_extract_var, [json_out], [img_out]
+        sd_image_from_character, [json_out], [img_out]
     )
     btn_chain.click(
         generate_connected_characters, [world_tb, persona_tb], [chained_out]