Update sozo_gen.py

sozo_gen.py

@@ -271,7 +271,7 @@ def sanitize_for_firebase_key(text: str) -> str:
 def generate_report_draft(buf, name: str, ctx: str, uid: str, project_id: str, bucket):
     logging.info(f"Generating report draft for project {project_id}")
     df = load_dataframe_safely(buf, name)
-    llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash-latest", google_api_key=API_KEY, temperature=0.1)
+    llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", google_api_key=API_KEY, temperature=0.1)
     ctx_dict = {"shape": df.shape, "columns": list(df.columns), "user_ctx": ctx}
     enhanced_ctx = enhance_data_context(df, ctx_dict)
     report_prompt = f"""
@@ -317,7 +317,7 @@ def generate_report_draft(buf, name: str, ctx: str, uid: str, project_id: str, b
 
 def generate_single_chart(df: pd.DataFrame, description: str, uid: str, project_id: str, bucket):
     logging.info(f"Generating single chart '{description}' for project {project_id}")
-    llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash-latest", google_api_key=API_KEY, temperature=0.1)
+    llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", google_api_key=API_KEY, temperature=0.1)
     chart_generator = ChartGenerator(llm, df)
     with tempfile.NamedTemporaryFile(suffix=".png", delete=False) as temp_file:
         img_path = Path(temp_file.name)
@@ -336,7 +336,7 @@ def generate_single_chart(df: pd.DataFrame, description: str, uid: str, project_
 
 def generate_video_from_project(df: pd.DataFrame, raw_md: str, uid: str, project_id: str, voice_model: str, bucket):
     logging.info(f"Generating video for project {project_id} with voice {voice_model}")
-    llm = ChatGoogleGenerativeAI(model="gemini-1.5-flash-latest", google_api_key=API_KEY, temperature=0.2)
+    llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", google_api_key=API_KEY, temperature=0.2)
     story_prompt = f"Based on the following report, create a script for a {VIDEO_SCENES}-scene video. Each scene must be separated by '[SCENE_BREAK]' and contain narration and one chart tag. Report: {raw_md}"
     script = llm.invoke(story_prompt).content
     scenes = [s.strip() for s in script.split("[SCENE_BREAK]") if s.strip()]