Update app/main.py

app/main.py

@@ -274,7 +274,7 @@ def generate_manuscript_excerpts(manuscript: str, prompt: str):
         print(f"Excerpt generation error: {e}")
         return ""
 
-# Generate a prompt based on manuscript, outline, characters, and prompt history
+# Generate a prompt based on the last 1000 words of the manuscript, outline, characters, and prompt history
 def generate_prompt(manuscript: str, outline: str, characters: str, project_id: str):
     # Initialize prompt components
     prompt_parts = []
@@ -297,10 +297,15 @@ def generate_prompt(manuscript: str, outline: str, characters: str, project_id:
         except Exception as e:
             print(f"Prompt history context extraction error: {e}")
 
-    # Extract key sentences from manuscript using semantic similarity
+    # Use the last 1000 words of the manuscript from the database
+    inputs = get_latest_inputs(project_id)
+    manuscript = inputs.get("manuscript") or manuscript
     if manuscript:
         try:
-            sentences = nltk.sent_tokenize(manuscript)[:50]  # Limit to 50 sentences for efficiency
+            words = manuscript.split()
+            # Take the last 1000 words, or all if fewer than 1000
+            last_1000_words = " ".join(words[-1000:]) if len(words) > 1000 else manuscript
+            sentences = nltk.sent_tokenize(last_1000_words)[:15]  # Limit to 15 sentences for efficiency
             if sentences:
                 sentence_embeddings = excerpt_model.encode(sentences)
                 avg_embedding = np.mean(sentence_embeddings, axis=0)
@@ -309,23 +314,30 @@ def generate_prompt(manuscript: str, outline: str, characters: str, project_id:
                 )
                 top_indices = similarities.argsort()[-3:][::-1]  # Top 3 sentences
                 key_sentences = [sentences[i] for i in top_indices]
-                context += "Recent Manuscript Context: " + " ".join(key_sentences) + "\n"
+                context += "Recent Manuscript Context (Last 1000 Words): " + " ".join(key_sentences) + "\n"
         except Exception as e:
             print(f"Manuscript context extraction error: {e}")
 
-    # Extract key plot points from outline
+    # Extract key plot points from outline, focusing on current context
     if outline:
         try:
             sentences = nltk.sent_tokenize(outline)[:20]  # Limit to 20 sentences
             if sentences:
                 sentence_embeddings = excerpt_model.encode(sentences)
-                avg_embedding = np.mean(sentence_embeddings, axis=0)
-                similarities = np.dot(sentence_embeddings, avg_embedding) / (
-                    np.linalg.norm(sentence_embeddings, axis=1) * np.linalg.norm(avg_embedding)
-                )
+                # If manuscript context exists, align outline with it
+                if manuscript:
+                    manuscript_embedding = excerpt_model.encode(last_1000_words)
+                    similarities = np.dot(sentence_embeddings, manuscript_embedding) / (
+                        np.linalg.norm(sentence_embeddings, axis=1) * np.linalg.norm(manuscript_embedding)
+                    )
+                else:
+                    avg_embedding = np.mean(sentence_embeddings, axis=0)
+                    similarities = np.dot(sentence_embeddings, avg_embedding) / (
+                        np.linalg.norm(sentence_embeddings, axis=1) * np.linalg.norm(avg_embedding)
+                    )
                 top_indices = similarities.argsort()[-2:][::-1]  # Top 2 sentences
                 key_points = [sentences[i] for i in top_indices]
-                context += "Key Plot Points: " + " ".join(key_points) + "\n"
+                context += "Relevant Plot Points: " + " ".join(key_points) + "\n"
         except Exception as e:
             print(f"Outline context extraction error: {e}")
 
@@ -346,10 +358,10 @@ def generate_prompt(manuscript: str, outline: str, characters: str, project_id:
             print(f"Character context extraction error: {e}")
 
     # Construct the prompt
-    prompt_parts.append("Write a detailed prose passage for a novel based on the following context.")
+    prompt_parts.append("Write a detailed prose passage for a novel based on the following context, focusing on the current scene.")
     if context:
         prompt_parts.append(context)
-    prompt_parts.append("Focus on advancing the story with vivid descriptions, character development, and alignment with the provided plot, character details, and previous prompts. Create a scene that feels immersive, consistent with the tone and style of the existing manuscript, and builds on prior narrative directions.")
+    prompt_parts.append("Advance the story with vivid descriptions and character development, aligning with the provided plot points, character details, and previous prompts. Ensure the scene is immersive, consistent with the tone and style of the manuscript's latest section, and continues the immediate narrative arc.")
 
     generated_prompt = "\n".join(prompt_parts)
     if len(generated_prompt) > MAX_MEDIUMTEXT_CHARS: