Update app.py

app.py

@@ -281,7 +281,7 @@ def _get_video_info_and_timestamps(media_path: str, sample_count: int) -> Tuple[
 
     timestamps: List[float] = []
     if duration > 0 and sample_count > 0:
-        actual_sample_count = min(sample_count, max(1, int(duration)))
+        actual_sample_count = min(sample_count, max(1, int(duration))) # Ensure sample_count doesn't exceed duration
         if actual_sample_count > 0:
             step = duration / (actual_sample_count + 1)
             timestamps = [step * (i + 1) for i in range(actual_sample_count)]
@@ -415,6 +415,9 @@ def upload_file_to_mistral(client, path: str, filename: str | None = None, purpo
                 progress(0.5 + 0.01 * attempt, desc=f"Uploading file to model service (attempt {attempt+1}/{max_retries})...")
 
             with open(path, "rb") as fh:
+                # The Mistral client's file upload typically expects a dict with 'file_name' and 'content'
+                # or a file-like object directly. Ensure compatibility here.
+                # Assuming the client expects 'file' parameter to be a dict as shown in placeholder
                 res = client.files.upload(file={"file_name": fname, "content": fh}, purpose=purpose)
 
             fid = getattr(res, "id", None) or (res.get("id") if isinstance(res, dict) else None)
@@ -518,7 +521,7 @@ def analyze_video_cohesive(client, video_path: str, prompt: str, progress=None)
         )
         return result, gallery_frame_paths
     except Exception as e:
-        print(f"Warning: Video upload/full analysis failed ({e}). Extracting frames as fallback...")
+        print(f"Warning: Video upload/full analysis failed ({type(e).__name__}: {e}). Extracting frames as fallback...")
         if progress is not None:
             progress(0.35, desc=f"Video upload failed ({type(e).__name__}). Extracting frames as fallback...")
 
@@ -527,7 +530,7 @@ def analyze_video_cohesive(client, video_path: str, prompt: str, progress=None)
         )
 
         if not frames_for_model_bytes:
-            return f"Error: could not upload video and no frames could be extracted for fallback. ({e})", []
+            return f"Error: could not upload video and no frames could be extracted for fallback. ({type(e).__name__}: {e})", []
 
         image_entries = []
         for i, fb in enumerate(frames_for_model_bytes, start=1):
@@ -850,8 +853,9 @@ def create_demo():
                 if not FFMPEG_BIN:
                     # If this is a video, and ffmpeg is missing, return early.
                     # Otherwise, proceed for image analysis.
-                    is_image_check, is_video_check = determine_media_type(url)
-                    if is_video_check:
+                    # Determine media type by extension from raw_media_path
+                    ext = ext_from_src(raw_media_path)
+                    if ext in VIDEO_EXTENSIONS:
                          return "error", "**Error:** FFmpeg is not found in your system PATH. Video analysis is unavailable. Please install FFmpeg.", current_main_preview_path, []
 
                 with open(raw_media_path, "rb") as f:
@@ -888,7 +892,7 @@ def create_demo():
                     progress(0.20, desc="Running image analysis")
                     result_text = analyze_image_structured(client, raw_bytes_for_analysis, prompt, progress=progress)
                 else:
-                    return "error", "Error: Could not definitively determine media type for analysis after byte inspection and extension check. Please check the URL.", current_main_preview_path, []
+                    return "error", "Error: Could not definitively determine media type for analysis after byte inspection and extension check. Please check the URL/file content.", current_main_preview_path, []
 
                 status = "done" if not (isinstance(result_text, str) and result_text.lower().startswith("error")) else "error"
                 return status, result_text, current_main_preview_path, generated_screenshot_paths