Update app.py

app.py

@@ -1,19 +1,23 @@
 from fastapi import FastAPI
 from pydantic import BaseModel
+from typing import Optional
 import subprocess, base64, os, uuid, shutil, whisper
    
 app = FastAPI()
 whisper_model = whisper.load_model("tiny")
 
+# 🚨 DYNAMIC SCHEMA
 class VideoJsonRequest(BaseModel):
     video_base64: str
+    num_frames: Optional[int] = 15          # Default to 15
+    get_transcript: Optional[bool] = True   # Default to True
 
 def to_b64(path):
     with open(path, "rb") as f: return base64.b64encode(f.read()).decode('utf-8')
 
 @app.get("/")
 async def index():
-    return {"success": True}
+    return {"success": True, "engine": "Dynamic Viral Cat Media Server"}
     
 @app.post("/process-video")
 async def process(req: VideoJsonRequest):
@@ -24,34 +28,46 @@ async def process(req: VideoJsonRequest):
     a_p = f"{tmp}/a.wav"
     try:
         with open(v_p, "wb") as f: f.write(base64.b64decode(req.video_base64))
+        
+        # Get Duration
         probe = subprocess.run(["ffprobe", "-v", "error", "-show_entries", "format=duration", "-of", "default=noprint_wrappers=1:nokey=1", v_p], capture_output=True, text=True).stdout
         dur = float(probe.strip() or 0)
+        
+        # 🚨 DYNAMIC FRAME MATH: Spreads requested frames evenly across duration
+        calc_fps = req.num_frames / max(dur, 1)
+        
         print("-------------------")
-        print(f"fps={5/max(dur,1)}")
-        print(f"fps={1/max(dur,1)}")
+        print(f"Requested Frames: {req.num_frames} | Duration: {dur:.2f}s | Calculated FPS: {calc_fps:.2f}")
+        print(f"Transcript requested: {req.get_transcript}")
         print("-------------------")
-        # Extract 15 frames
+
+        # Extract X frames
         subprocess.run(["ffmpeg", "-y", 
                         "-loglevel", "error", 
-                        "-i", v_p, "-vf", f"fps={3/max(dur,1)}", "-vframes", "15", "-q:v", "5", f"{tmp}/f_%03d.jpg"])
-        # Transcribe
-        subprocess.run(["ffmpeg", "-y",
-                        "-loglevel", "error", 
-                        "-i", v_p, "-vn", "-acodec", "pcm_s16le", "-ar", "16000", "-ac", "1", a_p])
-        #txt = whisper_model.transcribe(a_p)["text"].strip()
-        result = whisper_model.transcribe(a_p)
-
-        lines = [
-           f"[{s['start']:.2f}] {s['text'].strip()}"
-           for s in result["segments"]
-        ]
+                        "-i", v_p, "-vf", f"fps={calc_fps}", 
+                        "-vframes", str(req.num_frames), 
+                        "-q:v", "5", f"{tmp}/f_%03d.jpg"])
+        
+        # 🚨 CONDITIONAL TRANSCRIPT
+        txt = ""
+        if req.get_transcript:
+            subprocess.run(["ffmpeg", "-y",
+                            "-loglevel", "error", 
+                            "-i", v_p, "-vn", "-acodec", "pcm_s16le", "-ar", "16000", "-ac", "1", a_p])
+            
+            if os.path.exists(a_p):
+                result = whisper_model.transcribe(a_p)
+                lines = [f"[{s['start']:.2f}] {s['text'].strip()}" for s in result["segments"]]
+                txt = "\n".join(lines)
 
-        txt = "\n".join(lines)
+        # Gather frames
         f_names = sorted([f"{tmp}/{f}" for f in os.listdir(tmp) if f.startswith("f_")])
         imgs = [to_b64(f) for f in f_names]
+        
         print("-------------------")
-        print(f"length={len(imgs)}")
+        print(f"Successfully extracted {len(imgs)} images.")
         print("-------------------")
+        
         return {"success": True, "transcript": txt, "frames": imgs, "thumbnail": imgs[0] if imgs else None}
     except Exception as e: return {"success": False, "error": str(e)}
     finally: shutil.rmtree(tmp, ignore_errors=True)