rm async

app.py

@@ -256,7 +256,7 @@ class PicletGeneratorService:
     FLUX_SPACE = "black-forest-labs/FLUX.1-schnell"
 
     @staticmethod
-    async def generate_enhanced_caption(image_path: str, hf_token: str) -> str:
+    def generate_enhanced_caption(image_path: str, hf_token: str) -> str:
         """Generate detailed image description using JoyCaption
 
         Args:
@@ -271,7 +271,7 @@ class PicletGeneratorService:
             )
 
             print(f"Generating caption for image...")
-            result = await client.predict(
+            result = client.predict(
                 "/stream_chat",
                 handle_file(image_path),  # Wrap path so client uploads file
                 "Descriptive",  # caption_type
@@ -292,7 +292,7 @@ class PicletGeneratorService:
             raise Exception(f"Caption generation failed: {str(e)}")
 
     @staticmethod
-    async def generate_text_with_gpt(prompt: str, hf_token: str) -> str:
+    def generate_text_with_gpt(prompt: str, hf_token: str) -> str:
         """Generate text using GPT-OSS-120B"""
         try:
             print(f"Connecting to GPT-OSS space...")
@@ -302,7 +302,7 @@ class PicletGeneratorService:
             )
 
             print(f"Generating text...")
-            result = await client.predict(
+            result = client.predict(
                 "/chat",
                 prompt,  # message
                 [],  # history
@@ -331,7 +331,7 @@ class PicletGeneratorService:
             raise Exception(f"Text generation failed: {str(e)}")
 
     @staticmethod
-    async def generate_piclet_concept(caption: str, hf_token: str) -> dict:
+    def generate_piclet_concept(caption: str, hf_token: str) -> dict:
         """
         Generate complete Piclet concept from image caption
         Returns parsed concept with object name, variation, stats, etc.
@@ -386,7 +386,7 @@ CRITICAL RULES:
 - Monster Description must describe the CREATURE with references to the specific object's features
 - Primary Type must match the object category (machina for electronics, structure for buildings, etc.)"""
 
-        response_text = await PicletGeneratorService.generate_text_with_gpt(concept_prompt, hf_token)
+        response_text = PicletGeneratorService.generate_text_with_gpt(concept_prompt, hf_token)
 
         # Parse the concept
         return PicletGeneratorService.parse_concept(response_text)
@@ -471,7 +471,7 @@ CRITICAL RULES:
         }
 
     @staticmethod
-    async def generate_piclet_image(image_prompt: str, tier: str, hf_token: str) -> dict:
+    def generate_piclet_image(image_prompt: str, tier: str, hf_token: str) -> dict:
         """Generate Piclet image using Flux"""
         try:
             print(f"Connecting to Flux space...")
@@ -490,7 +490,7 @@ CRITICAL RULES:
             full_prompt = f"{image_prompt}\nNow generate an Pokémon Anime image of the monster in an idle pose with a plain dark-grey background. This is a {tier} tier monster with a {tier_descriptions.get(tier, tier_descriptions['medium'])}. The monster should not be attacking or in motion. The full monster must be visible within the frame."
 
             print(f"Generating image with prompt: {full_prompt[:100]}...")
-            result = await client.predict(
+            result = client.predict(
                 "/infer",
                 full_prompt,  # prompt
                 0,  # seed
@@ -784,7 +784,7 @@ def increment_scan_count(piclet_id: str, object_name: str) -> dict:
             "error": str(e)
         }
 
-async def generate_piclet(image, hf_token: str) -> dict:
+def generate_piclet(image, hf_token: str) -> dict:
     """
     Complete Piclet generation workflow - single endpoint
     Takes user's image and hf_token, returns generated Piclet with discovery status
@@ -821,11 +821,11 @@ async def generate_piclet(image, hf_token: str) -> dict:
 
         # Step 1: Generate caption
         print("Step 1/5: Generating image caption...")
-        caption = await PicletGeneratorService.generate_enhanced_caption(image_path, hf_token)
+        caption = PicletGeneratorService.generate_enhanced_caption(image_path, hf_token)
 
         # Step 2: Generate concept
         print("Step 2/5: Generating Piclet concept...")
-        concept_data = await PicletGeneratorService.generate_piclet_concept(caption, hf_token)
+        concept_data = PicletGeneratorService.generate_piclet_concept(caption, hf_token)
 
         object_name = concept_data['objectName']
         attributes = concept_data['attributes']
@@ -835,7 +835,7 @@ async def generate_piclet(image, hf_token: str) -> dict:
 
         # Step 3: Generate image
         print("Step 3/5: Generating Piclet image...")
-        image_result = await PicletGeneratorService.generate_piclet_image(
+        image_result = PicletGeneratorService.generate_piclet_image(
             image_prompt,
             stats['tier'],
             hf_token