Update app.py

app.py

@@ -11,9 +11,8 @@ MAX_NEW_TOKENS = 400
 TEMPERATURE = 0.4 # Note: min_p = 0.1 is kept as requested in the configuration from the previous prompt
 
 # --- Model Loading ---
-llm = None
 def load_llm():
-    global llm
+    llm = None
     print("Downloading model...")
     try:
         model_path = hf_hub_download(repo_id=MODEL_REPO, filename=MODEL_FILE)
@@ -27,7 +26,9 @@ def load_llm():
         print("Model loaded successfully!")
     except Exception as e:
         print(f"Error loading model: {e}")
-load_llm()
+    return llm
+llm_generator = load_llm()
+llm_analyst = load_llm()
 
 # --- Persona and Format Maps (Hardened Constraints) ---
 persona_map = {
@@ -63,10 +64,6 @@ def generate_poem(format_type, persona, topic, progress=gr.Progress()):
     # 1. VISUAL FEEDBACK & Error Checks
     progress(0, desc="Consulting the Muse...")
     time.sleep(0.2)
-    if not llm:
-        return "Error: Model not loaded."
-    if not topic:
-        return "Please enter a topic!"
     
     # --- POEM GENERATION ---
     progress(0.2, desc=f"Summoning {persona} and drafting poem...")
@@ -74,7 +71,7 @@ def generate_poem(format_type, persona, topic, progress=gr.Progress()):
     selected_constraint = format_map.get(format_type, "Write a poem.")
     
     full_poem_prompt = build_poem_prompt(selected_voice, selected_constraint, format_type, topic)
-    output = llm(
+    output = llm_generator(
         prompt=full_poem_prompt,
         max_tokens=MAX_NEW_TOKENS,
         temperature=TEMPERATURE,
@@ -84,7 +81,8 @@ def generate_poem(format_type, persona, topic, progress=gr.Progress()):
     )
     
     # Clean up output
-    poem_text = output['choices'][0]['text'].strip()
+    #poem_text = output['choices'][0]['text'].strip()
+    poem_text = output
     
     # Simple post-processing to clean up any residual instruction text if the model echoes it
     # We are trusting the instruction "Your output must contain ONLY the poem, nothing else."
@@ -95,10 +93,6 @@ def generate_poem(format_type, persona, topic, progress=gr.Progress()):
 
 # --- LLM Function: Step 2 (Analyze Poem - SIMPLIFIED) ---
 def analyze_poem(poem_text, progress=gr.Progress()):
-    if not llm:
-        return "Error: Model not loaded."
-    if not poem_text or poem_text.startswith("Error:") or poem_text.startswith("Please enter"):
-        return "Please generate a valid poem first!"
     
     progress(0, desc="Analyzing style for literary match...")
     time.sleep(0.2)
@@ -110,7 +104,7 @@ def analyze_poem(poem_text, progress=gr.Progress()):
         f"POEM:\n{poem_text}\n"
     )
     
-    output_poet = llm(
+    output_poet = llm_analyst(
         prompt=poet_prompt,
         max_tokens=200, # Keep this response short
         temperature=0.4, 
@@ -119,11 +113,8 @@ def analyze_poem(poem_text, progress=gr.Progress()):
     )
     
     # Clean up the output using simple string manipulation
-    poet_suggestion = output_poet['choices'][0]['text'].strip()
-    
-    # Clean up any potential instruction echo or partial output
-    if poet_suggestion.startswith("STRICT OUTPUT FORMAT:"):
-        poet_suggestion = poet_suggestion[len("STRICT OUTPUT FORMAT:"):].strip()
+    #poet_suggestion = output_poet['choices'][0]['text'].strip()
+    poet_suggestion = output_poet
     
     progress(1.0, desc="Analysis complete!")