Update app.py

app.py

@@ -119,7 +119,8 @@ TAG_RE = re.compile(
 extract_chart_tags = lambda t: list(dict.fromkeys(m.group("d").strip()
                                                   for m in TAG_RE.finditer(t or "")))
 
-re_scene = re.compile(r"^\s*scene\s*\d+[:.- ]*", re.I)
+# --- FIXED: Escaped the hyphen to treat it as a literal character ---
+re_scene = re.compile(r"^\s*scene\s*\d+[:\.- ]*", re.I)
 
 def clean_narration(txt: str) -> str:
     txt = re_scene.sub("", txt)
@@ -135,7 +136,6 @@ def placeholder_img() -> Image.Image:
 
 @st.cache_data(show_spinner="Generating image...")
 def generate_image_from_prompt(prompt: str) -> Image.Image:
-    # IMPROVED: Using your original model names for consistency with your environment.
     model_main = "gemini-2.0-flash-exp-image-generation"
     model_fallback = "gemini-2.0-flash-preview-image-generation"
     full_prompt = "A clean business-presentation illustration: " + prompt
@@ -152,7 +152,6 @@ def generate_image_from_prompt(prompt: str) -> Image.Image:
                     return Image.open(io.BytesIO(part.inline_data.data)).convert("RGB")
             return None
         except Exception:
-            # Silently fail to allow fallback
             return None
 
     img = fetch(model_main) or fetch(model_fallback)
@@ -184,10 +183,8 @@ def build_pdf(md: str, charts: Dict[str, str]) -> bytes:
 
 def generate_report_text(df: pd.DataFrame, ctx: str) -> Tuple[str, List[str]]:
     """Generates only the text part of the report. This is the fast, first step."""
-    # Using your original model name.
     llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", google_api_key=API_KEY, temperature=0.1)
     
-    # IMPROVED: Sending a summary instead of the full dataframe is more efficient and robust.
     ctx_dict = {
         "shape": df.shape, "columns": list(df.columns), "user_ctx": ctx or "General business analysis",
         "data_sample": df.head().to_dict('records'),
@@ -218,13 +215,11 @@ def generate_report_text(df: pd.DataFrame, ctx: str) -> Tuple[str, List[str]]:
 
 def generate_single_chart(description: str, df: pd.DataFrame) -> str:
     """Generates one chart using the agent and returns it as a base64 string. More reliable."""
-    # Using your original model name.
     llm = ChatGoogleGenerativeAI(model="gemini-2.0-flash", google_api_key=API_KEY, temperature=0.1)
     agent = create_pandas_dataframe_agent(
         llm=llm, df=df, verbose=False, allow_dangerous_code=True,
         agent_type="openai-functions", handle_parsing_errors=True
     )
-    # IMPROVED: A more explicit prompt for the agent leads to more reliable code generation.
     chart_prompt = f"""
     Your task is to generate Python code to create a single, static, professional chart using matplotlib based on the provided dataframe `df`.
     The user's request is: '{description}'.
@@ -255,7 +250,7 @@ def generate_single_chart(description: str, df: pd.DataFrame) -> str:
         except Exception as e:
             st.warning(f"Chart generation attempt failed: {e}")
             plt.close("all")
-    return None # Return None if all attempts fail
+    return None
 
 # ─── ANIMATION HELPERS (YOUR ORIGINAL CODE) ────────────────────────────────
 
@@ -264,7 +259,7 @@ def animate_image_fade(img_cv2: np.ndarray, dur: float, out: Path, fps: int = FP
     vid = cv2.VideoWriter(str(out), cv2.VideoWriter_fourcc(*"mp4v"), fps, (WIDTH, HEIGHT))
     blank = np.full_like(img_cv2, 255)
     for i in range(frames):
-        a = i / (frames - 1) # Go from 0 to 1
+        a = i / (frames - 1) if frames > 1 else 1.0
         vid.write(cv2.addWeighted(blank, 1 - a, img_cv2, a, 0))
     vid.release()
     return str(out)
@@ -350,17 +345,19 @@ def safe_chart(desc, df, dur, out):
         with plt.ioff():
             fig, ax = plt.subplots()
             try:
-                # Attempt a simple plot
                 df.select_dtypes(include=np.number).plot(ax=ax)
                 ax.set_title(desc)
-            except:
-                # If that fails, just show a text error on the image
+            except Exception:
                 ax.text(0.5, 0.5, 'Could not render chart', ha='center', va='center')
             
             p = Path(tempfile.gettempdir()) / f"{uuid.uuid4()}.png"
             fig.savefig(p, bbox_inches="tight"); plt.close(fig)
-            img = cv2.resize(cv2.imread(str(p)), (WIDTH, HEIGHT))
-            return animate_image_fade(img, dur, out)
+            img_path = str(p)
+            img = cv2.imread(img_path)
+            if img is None: # Handle case where image read fails
+                img = np.full((HEIGHT, WIDTH, 3), 230, dtype=np.uint8) # Fallback gray image
+            img_resized = cv2.resize(img, (WIDTH, HEIGHT))
+            return animate_image_fade(img_resized, dur, out)
 
 def concat_media(paths: List[str], out: Path, kind="video"):
     if not paths: return
@@ -369,8 +366,8 @@ def concat_media(paths: List[str], out: Path, kind="video"):
         for p in paths:
             if Path(p).exists() and Path(p).stat().st_size > 0:
                 f.write(f"file '{Path(p).resolve().as_posix()}'\n")
-    if lst_path.stat().st_size == 0:
-        lst_path.unlink()
+    if not lst_path.is_file() or lst_path.stat().st_size == 0:
+        if lst_path.is_file(): lst_path.unlink()
         return
     
     cmd = ["ffmpeg", "-y", "-f", "concat", "-safe", "0", "-i", str(lst_path), "-c", "copy", str(out)]
@@ -470,23 +467,25 @@ def generate_video(buf: bytes, name: str, ctx: str, key: str):
 mode = st.radio("Select Output Format:", ["Report (PDF)", "Video Narrative"], horizontal=True)
 upl = st.file_uploader("Upload CSV or Excel", type=["csv", "xlsx", "xls"])
 
-# Reset state if a new file is uploaded
-if upl and sha1_bytes(upl.getvalue()) != st.session_state.current_file_key:
-    st.session_state.report_md = None
-    st.session_state.chart_descs = []
-    st.session_state.generated_charts = {}
-    st.session_state.pdf_bytes = None
-    st.session_state.bundle = None
-    st.session_state.current_file_key = sha1_bytes(upl.getvalue())
-    df, err = load_dataframe_safely(upl.getvalue(), upl.name)
-    if err:
-        st.error(f"Error loading data: {err}")
-        st.session_state.df = None # Ensure df is cleared on error
-    else:
-        st.session_state.df = df
+if upl:
+    file_key = sha1_bytes(upl.getvalue())
+    if file_key != st.session_state.current_file_key:
+        st.session_state.report_md = None
+        st.session_state.chart_descs = []
+        st.session_state.generated_charts = {}
+        st.session_state.pdf_bytes = None
+        st.session_state.bundle = None
+        st.session_state.current_file_key = file_key
+        df, err = load_dataframe_safely(upl.getvalue(), upl.name)
+        if err:
+            st.error(f"Error loading data: {err}")
+            st.session_state.df = None
+        else:
+            st.session_state.df = df
+            st.rerun()
 
 if st.session_state.get("df") is not None:
-    with st.expander("📊 Data Preview"):
+    with st.expander("📊 Data Preview", expanded=True):
         st.dataframe(arrow_df(st.session_state.df.head()))
     ctx = st.text_area("Business context or specific instructions (optional)")
 
@@ -497,7 +496,7 @@ if st.session_state.get("df") is not None:
                 st.session_state.report_md = md
                 st.session_state.chart_descs = descs
             st.rerun()
-    else:
+    else: # Video Mode
         if st.button("🎬 Generate Video", type="primary"):
             st.warning("Video generation is a long process and will lock the UI.")
             with st.spinner("Generating video... This may take several minutes."):