Update app.py

app.py

@@ -18,6 +18,10 @@ HARDCODED_GEMINI_KEY = ""
 ENV_HF_TOKEN = os.environ.get("HF_TOKEN", "")
 ENV_GEMINI_KEY = os.environ.get("GEMINI_API_KEY", "")
 
+# Determine active keys (Prioritize Secrets > Hardcoded)
+ACTIVE_HF_TOKEN = ENV_HF_TOKEN if ENV_HF_TOKEN else HARDCODED_HF_TOKEN
+ACTIVE_GEMINI_KEY = ENV_GEMINI_KEY if ENV_GEMINI_KEY else HARDCODED_GEMINI_KEY
+
 def format_timecode(seconds, fps=25):
     """Converts seconds to HH:MM:SS:FF for Resolve/Premiere."""
     td = timedelta(seconds=seconds)
@@ -48,7 +52,7 @@ def generate_cmx_edl(edl_title, segments, source_name, fps=25):
 def call_gemini_for_edl(transcript_data, story_prompt, api_key):
     """Sends diarized, word-level transcript to Gemini Senior Editor."""
     if not api_key:
-        st.error("Gemini API Key is missing.")
+        st.error("Gemini API Key is missing. Set it in Space Secrets or app.py.")
         return None
         
     url = f"https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-preview-09-2025:generateContent?key={api_key}"
@@ -66,118 +70,4 @@ def call_gemini_for_edl(transcript_data, story_prompt, api_key):
     )
     
     payload = {
-        "contents": [{"parts": [{"text": f"Creative Brief: {story_prompt}\n\nTranscript Data:\n{json.dumps(transcript_data)}"}]}],
-        "systemInstruction": {"parts": [{"text": system_prompt}]},
-        "generationConfig": {"responseMimeType": "application/json"}
-    }
-    
-    try:
-        res = requests.post(url, json=payload)
-        res.raise_for_status()
-        return json.loads(res.json()['candidates'][0]['content']['parts'][0]['text'])
-    except Exception as e:
-        st.error(f"Senior Editor AI Error: {e}")
-        return None
-
-# --- Streamlit UI ---
-st.set_page_config(page_title="DocAI Editor", layout="wide")
-st.title("Documentary AI: Pipeline")
-
-with st.sidebar:
-    st.header("Settings")
-    
-    # Determine default values for inputs
-    def_gemini = ENV_GEMINI_KEY if ENV_GEMINI_KEY else HARDCODED_GEMINI_KEY
-    def_hf = ENV_HF_TOKEN if ENV_HF_TOKEN else HARDCODED_HF_TOKEN
-
-    input_gemini_key = st.text_input("Gemini API Key", type="password", value=def_gemini)
-    input_hf_token = st.text_input("HF Token (Diarization)", type="password", value=def_hf)
-    
-    active_api_key = input_gemini_key
-    active_hf_token = input_hf_token
-    fps = st.number_input("Timeline FPS", value=25)
-
-uploaded_file = st.file_uploader("Upload Video/Audio Clip", type=["mp4", "m4a", "wav", "mp3", "mov"])
-
-if uploaded_file:
-    # --- Step 1: Technical Processing ---
-    if "transcript" not in st.session_state:
-        if st.button("Step 1: Transcribe & Diarize"):
-            if not active_hf_token or "PASTE_YOUR_HF_TOKEN" in active_hf_token:
-                st.error("Please provide a valid Hugging Face Token in the sidebar or hardcode it in app.py.")
-            else:
-                with st.spinner("Processing... Extracting audio, identifying speakers, and transcribing:"):
-                    # Save local temp file
-                    with open("temp_input", "wb") as f:
-                        f.write(uploaded_file.getbuffer())
-                    
-                    # Optimized Audio: m4a, 64kbps, 16kHz, mono
-                    subprocess.run([
-                        "ffmpeg", "-i", "temp_input", 
-                        "-vn", "-acodec", "aac", "-ab", "64k", "-ar", "16000", "-ac", "1", 
-                        "audio_optimized.m4a", "-y"
-                    ])
-                    
-                    # 1. Diarization
-                    try:
-                        # Fix for pyannote.audio 3.0+: 'use_auth_token' is now just 'token'
-                        try:
-                            pipeline = Pipeline.from_pretrained(
-                                "pyannote/speaker-diarization@2.1", 
-                                token=active_hf_token
-                            )
-                        except TypeError:
-                            # Fallback for older versions of pyannote.audio
-                            pipeline = Pipeline.from_pretrained(
-                                "pyannote/speaker-diarization@2.1", 
-                                use_auth_token=active_hf_token
-                            )
-                            
-                        if torch.cuda.is_available():
-                            pipeline.to(torch.device("cuda"))
-                            
-                        diarization = pipeline("audio_optimized.m4a")
-                    except Exception as e:
-                        st.error(f"Diarization Error: {e}")
-                        st.stop()
-                    
-                    # 2. Whisper Transcription
-                    model = whisper.load_model("base")
-                    result = model.transcribe("audio_optimized.m4a", word_timestamps=True)
-                    
-                    # 3. Alignment
-                    final_segments = []
-                    for segment in result['segments']:
-                        mid_time = (segment['start'] + segment['end']) / 2
-                        speaker = "Unknown"
-                        for turn, _, speaker_id in diarization.itertracks(yield_label=True):
-                            if turn.start <= mid_time <= turn.end:
-                                speaker = speaker_id
-                                break
-                        
-                        final_segments.append({
-                            "speaker": speaker,
-                            "text": segment['text'],
-                            "start": segment['start'],
-                            "end": segment['end'],
-                            "words": segment.get('words', [])
-                        })
-                    
-                    st.session_state.transcript = final_segments
-                    st.success("Transcription and Diarization Complete.")
-
-    if "transcript" in st.session_state:
-        st.divider()
-        brief = st.text_area("Creative Brief", placeholder="e.g. Focus on the yeast story, remove the interviewer.")
-        
-        if st.button("Step 2: Create EDL"):
-            if not active_api_key:
-                st.error("Gemini API Key required.")
-            else:
-                with st.spinner("Analyzing..."):
-                    edl_segments = call_gemini_for_edl(st.session_state.transcript, brief, active_api_key)
-                    if edl_segments:
-                        final_edl = generate_cmx_edl("AI_Senior_Editor_Cut", edl_segments, uploaded_file.name, fps)
-                        st.subheader("EDL Preview")
-                        st.code(final_edl, language="text")
-                        st.download_button("Download EDL", data=final_edl, file_name="edit.edl")
+        "contents": [{"parts": [{"text": f"Creative Brief: {story_prompt}\n\nTranscript Data:\n{json