Fix v3: max_tokens=32000, shorter prompt, JSON repair for truncated responses

interface/app_gradio.py

@@ -80,21 +80,12 @@ def llm_chat(prompt, system=None, max_tokens=16000, temperature=0.4):
 def generate_outline(subject, num_slides, style, language, audience, image_mode):
     """Step 1: Ask LLM to generate the slide outline."""
 
-    prompt = f"""Generate a {num_slides}-slide presentation plan about "{subject}" in JSON format.
-Language for content: {language}. Style: {style}. Audience: {audience}.
+    prompt = f"""Generate a {num_slides}-slide presentation about "{subject}". Style: {style}. Language: {language}. Audience: {audience}.
+Reply ONLY raw JSON: {{"title":"...","slides":[{{"number":1,"title":"...","content":["point1","point2","point3"],"image_prompt":"english cinematic description","layout":"cover"}}]}}
+Layouts: cover(1st), content, comparison, timeline, closing(last).
+IMPORTANT: Keep each content point SHORT (max 20 words). Keep image_prompt under 30 words. No text in images."""
 
-Reply ONLY with valid JSON, nothing else. No explanation, no markdown code block, just raw JSON:
-{{"title":"Presentation title","slides":[{{"number":1,"title":"Slide title","content":["Rich detailed point 1","Rich detailed point 2","Rich detailed point 3","Rich detailed point 4"],"image_prompt":"cinematic english image description for AI generation, {style} style, no text","layout":"cover"}}]}}
-
-Rules:
-- Exactly {num_slides} slides
-- Each slide has 3-5 detailed content points with facts/data when possible
-- image_prompt: English, descriptive, cinematic, adapted to "{style}" style
-- layout values: cover (slide 1), content, comparison, timeline, quote, closing (last slide)
-- Content language: {language}
-- Make it rich, informative, with specific facts and details"""
-
-    result = llm_chat(prompt, max_tokens=16000)
+    result = llm_chat(prompt, max_tokens=32000)
 
     # Extract JSON from response
     try:
@@ -105,6 +96,31 @@ Rules:
         else:
             outline = json.loads(result)
     except json.JSONDecodeError as e:
+        # Try to repair truncated JSON by closing brackets
+        repaired = result.rstrip()
+        # Remove trailing incomplete values
+        for _ in range(10):
+            try:
+                # Try adding closing brackets
+                for fix in ['}]}', '"}]}', '"]},{"number":99,"title":"FIN","content":[""],"image_prompt":"","layout":"closing"}]}']:
+                    try:
+                        test = repaired + fix
+                        json_match = re.search(r'\{[\s\S]*\}', test)
+                        if json_match:
+                            outline = json.loads(json_match.group())
+                            # Remove dummy slides if added
+                            outline["slides"] = [s for s in outline.get("slides",[]) if s.get("number") != 99]
+                            return outline
+                    except:
+                        continue
+                # Cut last incomplete entry and retry
+                last_brace = repaired.rfind('{')
+                if last_brace > 0:
+                    repaired = repaired[:last_brace].rstrip().rstrip(',')
+                else:
+                    break
+            except:
+                break
         raise RuntimeError(f"Le LLM n'a pas retourné du JSON valide.\n\nRéponse:\n{result[:2000]}")
 
     return outline