Update app.py

app.py

@@ -1,460 +1,258 @@
-# import streamlit as st
-# from transformers import AutoProcessor, Wav2Vec2ForCTC
-# import torch
-# import librosa
-# import os
-# from pydub import AudioSegment
-# from moviepy.editor import VideoFileClip
-# from google import genai
-# from google.genai import types
-
-# # ----------- Configuration -----------
-# model_id = "facebook/mms-1b-l1107"
-# lang_code = "urd-script_arabic"
-# api_key = "AIzaSyBEWWn32PxVEaUsoe67GJOEpF4FQT87Kxo"  # ⚠️ Replace with st.secrets for production
-
-# # ----------- Load Processor and Model -----------
-# @st.cache_resource
-# def load_model_and_processor():
-#     processor = AutoProcessor.from_pretrained(model_id, target_lang=lang_code)
-#     model = Wav2Vec2ForCTC.from_pretrained(
-#         model_id,
-#         target_lang=lang_code,
-#         ignore_mismatched_sizes=True
-#     )
-#     model.load_adapter(lang_code)
-#     return processor, model
-
-# processor, model = load_model_and_processor()
-
-# # ----------- Audio Conversion -----------
-# def get_wav_from_input(file_path, output_path="converted.wav"):
-#     ext = os.path.splitext(file_path)[-1].lower()
-#     if ext in [".mp4", ".mkv", ".avi", ".mov"]:
-#         video = VideoFileClip(file_path)
-#         video.audio.write_audiofile(output_path, fps=16000)
-#     elif ext in [".mp3", ".aac", ".flac", ".ogg", ".m4a"]:
-#         audio = AudioSegment.from_file(file_path)
-#         audio = audio.set_frame_rate(16000).set_channels(1)
-#         audio.export(output_path, format="wav")
-#     elif ext == ".wav":
-#         audio = AudioSegment.from_wav(file_path)
-#         audio.export(output_path, format="wav")
-#     else:
-#         raise ValueError("Unsupported file format.")
-#     return output_path
-
-# # ----------- Transcription -----------
-# def transcribe(file_path):
-#     wav_path = get_wav_from_input(file_path)
-#     audio, sr = librosa.load(wav_path, sr=16000)
-#     inputs = processor(audio, sampling_rate=sr, return_tensors="pt", padding=True)
-#     with torch.no_grad():
-#         logits = model(**inputs).logits
-#     pred_ids = torch.argmax(logits, dim=-1)
-#     return processor.batch_decode(pred_ids)[0]
-
-# # ----------- Gemini Analysis -----------
-# def analyze_transcript(transcript):
-#     client = genai.Client(api_key=api_key)
-
-#     system_instr = """
-# You are a speech analyst. The following transcription is in Urdu and contains no punctuation — your first task is to correct the transcript by segmenting it into grammatically correct sentences.
-
-# Then:
-# 1. Translate the corrected Urdu transcript into English.
-# 2. Determine whether the transcript involves a single speaker or multiple speakers.
-# 3. If multiple speakers are detected, perform diarization by segmenting the transcript with clear speaker labels.
-
-# ⚠️ Format the segmented transcript *exactly* like this:
-
-# **Segmented Transcript**
-
-# **Urdu:**
-# Person 01:
-# [Urdu line here]
-
-# Person 02:
-# [Urdu line here]
-
-# ...
-
-# **English:**
-# Person 01:
-# [English line here]
-
-# Person 02:
-# [English line here]
-
-# ...
-
-# After that, provide your analysis in the following format:
-
-# **Speaker-wise Analysis**
-# [One or two sentences per speaker about tone, emotion, behavior]
-
-# **Sentiment and Communication Style**
-# [Concise overall tone: e.g., friendly, formal, tense, etc.]
-
-# **Summary of Discussion**
-# [A 2–3 line summary of what the speakers talked about, in English]
-# """
-
-#     response = client.models.generate_content(
-#         model="gemini-2.5-flash",
-#         contents=[transcript],
-#         config=types.GenerateContentConfig(
-#             system_instruction=system_instr,
-#             temperature=0.0
-#         )
-#     )
-#     return response.text
-
-# # ----------- Format Display Helper -----------
-# def format_transcript_block(text: str) -> str:
-#     lines = text.split("Person ")
-#     formatted = ""
-#     for line in lines:
-#         line = line.strip()
-#         if not line:
-#             continue
-#         if line.startswith("01:") or line.startswith("02:"):
-#             formatted += f"\n**Person {line[:2]}**:\n{line[3:].strip()}\n\n"
-#         else:
-#             formatted += f"{line.strip()}\n\n"
-#     return formatted
-
-# # ----------- Streamlit UI -----------
-# # Styled Header
-# st.markdown("""
-#     <div style="text-align: left; padding-bottom: 1rem;">
-#         <h1 style='color:#1f77b4; font-size: 2.5em; font-weight: 800; margin-bottom: 0.2em;'>
-#             🎙️ Urdu Audio & Video Speech Analyzer
-#         </h1>
-#         <p style='color: #CCCCCC; font-size: 1.05em; margin-top: 0;'>
-#             Upload Urdu audio or video to get structured transcription, speaker diarization, and smart AI analysis.
-#         </p>
-#     </div>
-# """, unsafe_allow_html=True)
-
-# # File Upload
-# st.markdown("### 📂 Upload an audio or video file")
-# with st.container():
-#     uploaded_file = st.file_uploader(
-#         label="",
-#         type=["mp3", "mp4", "wav", "mkv", "aac", "ogg", "m4a", "flac"],
-#         label_visibility="collapsed"
-#     )
-
-# if uploaded_file is not None:
-#     with st.spinner("⏳ Transcribing..."):
-#         file_name = uploaded_file.name
-#         temp_path = f"temp_input{os.path.splitext(file_name)[-1]}"
-#         with open(temp_path, "wb") as f:
-#             f.write(uploaded_file.read())
-#         transcript = transcribe(temp_path)
-
-#     st.markdown("### 📝 Raw Urdu Transcription")
-#     st.text(transcript)
-
-#     with st.spinner("🔍 Analyzing with Gemini..."):
-#         report = analyze_transcript(transcript)
-
-#     # Extract Segmented Urdu and English
-#     segmented_urdu = ""
-#     segmented_english = ""
-#     analysis_only = ""
-
-#     if "Urdu:" in report and "English:" in report:
-#         urdu_start = report.find("Urdu:")
-#         english_start = report.find("English:")
-#         segmented_urdu = report[urdu_start + len("Urdu:"):english_start].strip()
-
-#         english_section = report[english_start + len("English:"):].strip()
-#         if "**Speaker-wise Analysis**" in english_section:
-#             parts = english_section.split("**Speaker-wise Analysis**")
-#             segmented_english = parts[0].strip()
-#             analysis_only = "**Speaker-wise Analysis**" + parts[1].strip()
-#         else:
-#             segmented_english = english_section.strip()
-#             analysis_only = "⚠️ Could not extract structured analysis."
-
-#     # Show Segmented Transcript
-#     if segmented_urdu and segmented_english:
-#         st.markdown("### 🗣️ Segmented Transcript")
-#         col1, col2 = st.columns(2)
-
-#         with col1:
-#             st.markdown("####  Urdu")
-#             st.markdown(format_transcript_block(segmented_urdu))
-
-#         with col2:
-#             st.markdown("####  English")
-#             st.markdown(format_transcript_block(segmented_english))
-
-#     # Show Gemini Analysis Only (No transcript repeat)
-#     if analysis_only:
-#         st.markdown("### 🧠 Gemini Analysis Summary")
-#         st.markdown(analysis_only)
-# app.py
-
-# api_key = "AIzaSyBEWWn32PxVEaUsoe67GJOEpF4FQT87Kxo"
-
-import io, os, numpy as np, streamlit as st, librosa, torch, soundfile as sf
-from transformers import AutoProcessor, Wav2Vec2ForCTC
-from pydub import AudioSegment
-from moviepy.editor import VideoFileClip
-from google import genai
-from google.genai import types
-
-# ✅ programmatic Start/Stop mic (no WebRTC)
-from streamlit_mic_recorder import mic_recorder
-
-# ---------------- Config ----------------
-st.set_page_config(page_title="Urdu Speech Analyzer", page_icon="🎙️", layout="wide")
-PAGE_TITLE = "🎙️ Urdu Audio & Video Speech Analyzer"
-model_id = "facebook/mms-1b-l1107"
-lang_code = "urd-script_arabic"
-api_key = "AIzaSyBEWWn32PxVEaUsoe67GJOEpF4FQT87Kxo"  # hard-coded as requested
-
-# ---------------- Model ----------------
-@st.cache_resource
-def load_model_and_processor():
-    processor = AutoProcessor.from_pretrained(model_id, target_lang=lang_code)
-    model = Wav2Vec2ForCTC.from_pretrained(
-        model_id, target_lang=lang_code, ignore_mismatched_sizes=True
-    )
-    model.load_adapter(lang_code)
-    return processor, model
-
-processor, model = load_model_and_processor()
-
-# ---------------- Helpers ----------------
-def get_wav_from_input(file_path, output_path="converted.wav"):
-    ext = os.path.splitext(file_path)[-1].lower()
-    if ext in [".mp4", ".mkv", ".avi", ".mov"]:
-        video = VideoFileClip(file_path)
-        video.audio.write_audiofile(output_path, fps=16000)
-    elif ext in [".mp3", ".aac", ".flac", ".ogg", ".m4a"]:
-        audio = AudioSegment.from_file(file_path)
-        audio = audio.set_frame_rate(16000).set_channels(1)
-        audio.export(output_path, format="wav")
-    elif ext == ".wav":
-        audio = AudioSegment.from_wav(file_path)
-        audio = audio.set_frame_rate(16000).set_channels(1)
-        audio.export(output_path, format="wav")
-    else:
-        raise ValueError("Unsupported file format.")
-    return output_path
-
-def save_wav_resampled(audio_f32: np.ndarray, sr_in: int, path: str):
-    if sr_in != 16000:
-        audio_f32 = librosa.resample(audio_f32, orig_sr=sr_in, target_sr=16000)
-    audio_f32 = librosa.util.normalize(audio_f32)
-    sf.write(path, audio_f32.astype(np.float32), 16000)
-
-def transcribe(wav_path) -> str:
-    audio, sr = librosa.load(wav_path, sr=16000, mono=True)
-    inputs = processor(audio, sampling_rate=sr, return_tensors="pt", padding=True)
-    with torch.no_grad():
-        logits = model(**inputs).logits
-    pred_ids = torch.argmax(logits, dim=-1)
-    return processor.batch_decode(pred_ids)[0]
-
-def analyze_transcript(transcript: str) -> str:
-    client = genai.Client(api_key=api_key)
-    system_instr = """
-You are a speech analyst. The following transcription is in Urdu and contains no punctuation — your first task is to correct the transcript by segmenting it into grammatically correct sentences.
-
-Then:
-1. Translate the corrected Urdu transcript into English.
-2. Determine whether the transcript involves a single speaker or multiple speakers.
-3. If multiple speakers are detected, perform diarization by segmenting the transcript with clear speaker labels.
-
-⚠️ Format the segmented transcript *exactly* like this:
-
-**Segmented Transcript**
-
-**Urdu:**
-Person 01:
-[Urdu line here]
-
-Person 02:
-[Urdu line here]
-
-...
-
-**English:**
-Person 01:
-[English line here]
-
-Person 02:
-[English line here]
-
-...
-
-After that, provide your analysis in the following format:
-
-**Speaker-wise Analysis**
-[One or two sentences per speaker about tone, emotion, behavior]
-
-**Sentiment and Communication Style**
-[Concise overall tone: e.g., friendly, formal, tense, etc.]
-
-**Summary of Discussion**
-[A 2–3 line summary of what the speakers talked about, in English]
-"""
-    resp = client.models.generate_content(
-        model="gemini-2.5-flash",
-        contents=[transcript],
-        config=types.GenerateContentConfig(system_instruction=system_instr, temperature=0.0)
-    )
-    return resp.text
-
-def format_transcript_block(text: str) -> str:
-    lines = text.split("Person ")
-    out = ""
-    for line in lines:
-        line = line.strip()
-        if not line:
-            continue
-        if line.startswith("01:") or line.startswith("02:"):
-            out += f"\n**Person {line[:2]}**:\n{line[3:].strip()}\n\n"
-        else:
-            out += f"{line}\n\n"
-    return out
-
-# ---------------- Header ----------------
-st.markdown(f"""
-    <div style="text-align: left; padding-bottom: 1rem;">
-        <h1 style='color:#1f77b4; font-size: 2.5em; font-weight: 800; margin-bottom: 0.2em;'>
-            {PAGE_TITLE}
-        </h1>
-        <p style='color: #7c8a98; font-size: 1.05em; margin-top: 0;'>
-            Record or upload Urdu speech for structured transcription, diarization, and smart AI analysis.
-        </p>
-    </div>
-""", unsafe_allow_html=True)
-
-# ================= Mic: true Start/Stop + narrow Analyze =================
-st.markdown("### 🎤 Live recording")
-
-# The component renders **Start** and **Stop** buttons and keeps recording until you press Stop.
-rec = mic_recorder(
-    start_prompt="▶️ Start",
-    stop_prompt="⏹️ Stop",
-    just_once=False,       # allow multiple recordings in a session
-    key="recorder",
-    format="wav"           # returns WAV bytes
-)
-
-# `rec` returns after Stop. Different versions return bytes or a dict — handle both.
-audio_bytes, sr_in = None, 44100
-if rec is not None:
-    if isinstance(rec, dict) and "bytes" in rec:
-        audio_bytes = rec["bytes"]
-        sr_in = int(rec.get("sample_rate", 44100))
-    elif isinstance(rec, (bytes, bytearray)):
-        audio_bytes = rec
-        sr_in = 44100  # component default
-    else:
-        # fallback: try to extract .get("audio") etc if lib changes
-        audio_bytes = rec.get("audio") if isinstance(rec, dict) else None
-
-if audio_bytes:
-    st.success("Audio captured.")
-    # Convert to mono float32
-    data, sr_read = sf.read(io.BytesIO(audio_bytes), dtype="float32", always_2d=False)
-    if data.ndim > 1:
-        data = data.mean(axis=1)
-    if sr_read:  # prefer the rate embedded in the WAV
-        sr_in = sr_read
-
-    # Save as 16 kHz mono for the model
-    tmp_wav = "mic_recording.wav"
-    save_wav_resampled(data, sr_in, tmp_wav)
-
-    # Minimal playback (no waveform)
-    st.audio(audio_bytes, format="audio/wav")
-    st.caption(f"Duration: {data.size / sr_in:.2f} s")
-
-    # Slim Analyze button (not full width)
-    if st.button("🔍 Analyze", type="primary"):
-            with st.spinner("⏳ Transcribing & analyzing..."):
-                transcript = transcribe(tmp_wav)     # raw not displayed
-                report = analyze_transcript(transcript)
-
-            segmented_urdu = segmented_english = analysis_only = ""
-            if "Urdu:" in report and "English:" in report:
-                u0 = report.find("Urdu:")
-                e0 = report.find("English:")
-                segmented_urdu = report[u0 + len("Urdu:"):e0].strip()
-                english_section = report[e0 + len("English:"):].strip()
-                if "**Speaker-wise Analysis**" in english_section:
-                    parts = english_section.split("**Speaker-wise Analysis**")
-                    segmented_english = parts[0].strip()
-                    analysis_only = "**Speaker-wise Analysis**" + parts[1].strip()
-                else:
-                    segmented_english = english_section.strip()
-                    analysis_only = "⚠️ Could not extract structured analysis."
-
-            if segmented_urdu or segmented_english:
-                st.markdown("### 🗣️ Segmented Transcript")
-                c1, c2 = st.columns(2)
-                with c1:
-                    st.markdown("#### Urdu")
-                    st.markdown(format_transcript_block(segmented_urdu) if segmented_urdu else "_(none)_")
-                with c2:
-                    st.markdown("#### English")
-                    st.markdown(format_transcript_block(segmented_english) if segmented_english else "_(none)_")
-            if analysis_only:
-                st.markdown("### 🧠 Gemini Analysis Summary")
-                st.markdown(analysis_only)
-
-st.markdown("---")
-
-# ================= Upload (unchanged) =================
-st.markdown("### 📂 Or upload an audio/video file")
-uploaded_file = st.file_uploader(
-    label="",
-    type=["mp3", "mp4", "wav", "mkv", "aac", "ogg", "m4a", "flac"],
-    label_visibility="collapsed"
-)
-if uploaded_file is not None:
-    with st.spinner("⏳ Transcribing..."):
-        file_name = uploaded_file.name
-        temp_path = f"temp_input{os.path.splitext(file_name)[-1]}"
-        with open(temp_path, "wb") as f:
-            f.write(uploaded_file.read())
-        wav_path = get_wav_from_input(temp_path)
-        transcript = transcribe(wav_path)
-
-    with st.spinner("🔍 Analyzing with Gemini..."):
-        report = analyze_transcript(transcript)
-
-    segmented_urdu = segmented_english = analysis_only = ""
-    if "Urdu:" in report and "English:" in report:
-        u0 = report.find("Urdu:")
-        e0 = report.find("English:")
-        segmented_urdu = report[u0 + len("Urdu:"):e0].strip()
-        english_section = report[e0 + len("English:"):].strip()
-        if "**Speaker-wise Analysis**" in english_section:
-            parts = english_section.split("**Speaker-wise Analysis**")
-            segmented_english = parts[0].strip()
-            analysis_only = "**Speaker-wise Analysis**" + parts[1].strip()
-        else:
-            segmented_english = english_section.strip()
-            analysis_only = "⚠️ Could not extract structured analysis."
-
-    if segmented_urdu or segmented_english:
-        st.markdown("### 🗣️ Segmented Transcript")
-        c1, c2 = st.columns(2)
-        with c1:
-            st.markdown("#### Urdu")
-            st.markdown(format_transcript_block(segmented_urdu) if segmented_urdu else "_(none)_")
-        with c2:
-            st.markdown("#### English")
-            st.markdown(format_transcript_block(segmented_english) if segmented_english else "_(none)_")
-    if analysis_only:
-        st.markdown("### 🧠 Gemini Analysis Summary")
-        st.markdown(analysis_only)
+import io, os, numpy as np, streamlit as st, librosa, torch, soundfile as sf
+from transformers import AutoProcessor, Wav2Vec2ForCTC
+from pydub import AudioSegment
+from moviepy.editor import VideoFileClip
+from google import genai
+from google.genai import types
+
+# ✅ programmatic Start/Stop mic (no WebRTC)
+from streamlit_mic_recorder import mic_recorder
+
+# ---------------- Config ----------------
+st.set_page_config(page_title="Urdu Speech Analyzer", page_icon="🎙️", layout="wide")
+PAGE_TITLE = "🎙️ Urdu Audio & Video Speech Analyzer"
+model_id = "facebook/mms-1b-l1107"
+lang_code = "urd-script_arabic"
+api_key = "AIzaSyBEWWn32PxVEaUsoe67GJOEpF4FQT87Kxo"  # hard-coded as requested
+
+# ---------------- Model ----------------
+@st.cache_resource
+def load_model_and_processor():
+    processor = AutoProcessor.from_pretrained(model_id, target_lang=lang_code)
+    model = Wav2Vec2ForCTC.from_pretrained(
+        model_id, target_lang=lang_code, ignore_mismatched_sizes=True
+    )
+    model.load_adapter(lang_code)
+    return processor, model
+
+processor, model = load_model_and_processor()
+
+# ---------------- Helpers ----------------
+def get_wav_from_input(file_path, output_path="converted.wav"):
+    ext = os.path.splitext(file_path)[-1].lower()
+    if ext in [".mp4", ".mkv", ".avi", ".mov"]:
+        video = VideoFileClip(file_path)
+        video.audio.write_audiofile(output_path, fps=16000)
+    elif ext in [".mp3", ".aac", ".flac", ".ogg", ".m4a"]:
+        audio = AudioSegment.from_file(file_path)
+        audio = audio.set_frame_rate(16000).set_channels(1)
+        audio.export(output_path, format="wav")
+    elif ext == ".wav":
+        audio = AudioSegment.from_wav(file_path)
+        audio = audio.set_frame_rate(16000).set_channels(1)
+        audio.export(output_path, format="wav")
+    else:
+        raise ValueError("Unsupported file format.")
+    return output_path
+
+def save_wav_resampled(audio_f32: np.ndarray, sr_in: int, path: str):
+    if sr_in != 16000:
+        audio_f32 = librosa.resample(audio_f32, orig_sr=sr_in, target_sr=16000)
+    audio_f32 = librosa.util.normalize(audio_f32)
+    sf.write(path, audio_f32.astype(np.float32), 16000)
+
+def transcribe(wav_path) -> str:
+    audio, sr = librosa.load(wav_path, sr=16000, mono=True)
+    inputs = processor(audio, sampling_rate=sr, return_tensors="pt", padding=True)
+    with torch.no_grad():
+        logits = model(**inputs).logits
+    pred_ids = torch.argmax(logits, dim=-1)
+    return processor.batch_decode(pred_ids)[0]
+
+def analyze_transcript(transcript: str) -> str:
+    client = genai.Client(api_key=api_key)
+    system_instr = """
+You are a speech analyst. The following transcription is in Urdu and contains no punctuation — your first task is to correct the transcript by segmenting it into grammatically correct sentences.
+
+Then:
+1. Translate the corrected Urdu transcript into English.
+2. Determine whether the transcript involves a single speaker or multiple speakers.
+3. If multiple speakers are detected, perform diarization by segmenting the transcript with clear speaker labels.
+
+⚠️ Format the segmented transcript *exactly* like this:
+
+**Segmented Transcript**
+
+**Urdu:**
+Person 01:
+[Urdu line here]
+
+Person 02:
+[Urdu line here]
+
+...
+
+**English:**
+Person 01:
+[English line here]
+
+Person 02:
+[English line here]
+
+...
+
+After that, provide your analysis in the following format:
+
+**Speaker-wise Analysis**
+[One or two sentences per speaker about tone, emotion, behavior]
+
+**Sentiment and Communication Style**
+[Concise overall tone: e.g., friendly, formal, tense, etc.]
+
+**Summary of Discussion**
+[A 2–3 line summary of what the speakers talked about, in English]
+"""
+    resp = client.models.generate_content(
+        model="gemini-2.5-flash",
+        contents=[transcript],
+        config=types.GenerateContentConfig(system_instruction=system_instr, temperature=0.0)
+    )
+    return resp.text
+
+def format_transcript_block(text: str) -> str:
+    lines = text.split("Person ")
+    out = ""
+    for line in lines:
+        line = line.strip()
+        if not line:
+            continue
+        if line.startswith("01:") or line.startswith("02:"):
+            out += f"\n**Person {line[:2]}**:\n{line[3:].strip()}\n\n"
+        else:
+            out += f"{line}\n\n"
+    return out
+
+# ---------------- Header ----------------
+st.markdown(f"""
+    <div style="text-align: left; padding-bottom: 1rem;">
+        <h1 style='color:#1f77b4; font-size: 2.5em; font-weight: 800; margin-bottom: 0.2em;'>
+            {PAGE_TITLE}
+        </h1>
+        <p style='color: #7c8a98; font-size: 1.05em; margin-top: 0;'>
+            Record or upload Urdu speech for structured transcription, diarization, and smart AI analysis.
+        </p>
+    </div>
+""", unsafe_allow_html=True)
+
+# ================= Mic: true Start/Stop + narrow Analyze =================
+st.markdown("### 🎤 Live recording")
+
+# The component renders **Start** and **Stop** buttons and keeps recording until you press Stop.
+rec = mic_recorder(
+    start_prompt="▶️ Start",
+    stop_prompt="⏹️ Stop",
+    just_once=False,       # allow multiple recordings in a session
+    key="recorder",
+    format="wav"           # returns WAV bytes
+)
+
+# `rec` returns after Stop. Different versions return bytes or a dict — handle both.
+audio_bytes, sr_in = None, 44100
+if rec is not None:
+    if isinstance(rec, dict) and "bytes" in rec:
+        audio_bytes = rec["bytes"]
+        sr_in = int(rec.get("sample_rate", 44100))
+    elif isinstance(rec, (bytes, bytearray)):
+        audio_bytes = rec
+        sr_in = 44100  # component default
+    else:
+        # fallback: try to extract .get("audio") etc if lib changes
+        audio_bytes = rec.get("audio") if isinstance(rec, dict) else None
+
+if audio_bytes:
+    st.success("Audio captured.")
+    # Convert to mono float32
+    data, sr_read = sf.read(io.BytesIO(audio_bytes), dtype="float32", always_2d=False)
+    if data.ndim > 1:
+        data = data.mean(axis=1)
+    if sr_read:  # prefer the rate embedded in the WAV
+        sr_in = sr_read
+
+    # Save as 16 kHz mono for the model
+    tmp_wav = "mic_recording.wav"
+    save_wav_resampled(data, sr_in, tmp_wav)
+
+    # Minimal playback (no waveform)
+    st.audio(audio_bytes, format="audio/wav")
+    st.caption(f"Duration: {data.size / sr_in:.2f} s")
+
+    # Slim Analyze button (not full width)
+    if st.button("🔍 Analyze", type="primary"):
+            with st.spinner("⏳ Transcribing & analyzing..."):
+                transcript = transcribe(tmp_wav)     # raw not displayed
+                report = analyze_transcript(transcript)
+
+            segmented_urdu = segmented_english = analysis_only = ""
+            if "Urdu:" in report and "English:" in report:
+                u0 = report.find("Urdu:")
+                e0 = report.find("English:")
+                segmented_urdu = report[u0 + len("Urdu:"):e0].strip()
+                english_section = report[e0 + len("English:"):].strip()
+                if "**Speaker-wise Analysis**" in english_section:
+                    parts = english_section.split("**Speaker-wise Analysis**")
+                    segmented_english = parts[0].strip()
+                    analysis_only = "**Speaker-wise Analysis**" + parts[1].strip()
+                else:
+                    segmented_english = english_section.strip()
+                    analysis_only = "⚠️ Could not extract structured analysis."
+
+            if segmented_urdu or segmented_english:
+                st.markdown("### 🗣️ Segmented Transcript")
+                c1, c2 = st.columns(2)
+                with c1:
+                    st.markdown("#### Urdu")
+                    st.markdown(format_transcript_block(segmented_urdu) if segmented_urdu else "_(none)_")
+                with c2:
+                    st.markdown("#### English")
+                    st.markdown(format_transcript_block(segmented_english) if segmented_english else "_(none)_")
+            if analysis_only:
+                st.markdown("### 🧠 Gemini Analysis Summary")
+                st.markdown(analysis_only)
+
+st.markdown("---")
+
+# ================= Upload (unchanged) =================
+st.markdown("### 📂 Or upload an audio/video file")
+uploaded_file = st.file_uploader(
+    label="",
+    type=["mp3", "mp4", "wav", "mkv", "aac", "ogg", "m4a", "flac"],
+    label_visibility="collapsed"
+)
+if uploaded_file is not None:
+    with st.spinner("⏳ Transcribing..."):
+        file_name = uploaded_file.name
+        temp_path = f"temp_input{os.path.splitext(file_name)[-1]}"
+        with open(temp_path, "wb") as f:
+            f.write(uploaded_file.read())
+        wav_path = get_wav_from_input(temp_path)
+        transcript = transcribe(wav_path)
+
+    with st.spinner("🔍 Analyzing with Gemini..."):
+        report = analyze_transcript(transcript)
+
+    segmented_urdu = segmented_english = analysis_only = ""
+    if "Urdu:" in report and "English:" in report:
+        u0 = report.find("Urdu:")
+        e0 = report.find("English:")
+        segmented_urdu = report[u0 + len("Urdu:"):e0].strip()
+        english_section = report[e0 + len("English:"):].strip()
+        if "**Speaker-wise Analysis**" in english_section:
+            parts = english_section.split("**Speaker-wise Analysis**")
+            segmented_english = parts[0].strip()
+            analysis_only = "**Speaker-wise Analysis**" + parts[1].strip()
+        else:
+            segmented_english = english_section.strip()
+            analysis_only = "⚠️ Could not extract structured analysis."
+
+    if segmented_urdu or segmented_english:
+        st.markdown("### 🗣️ Segmented Transcript")
+        c1, c2 = st.columns(2)
+        with c1:
+            st.markdown("#### Urdu")
+            st.markdown(format_transcript_block(segmented_urdu) if segmented_urdu else "_(none)_")
+        with c2:
+            st.markdown("#### English")
+            st.markdown(format_transcript_block(segmented_english) if segmented_english else "_(none)_")
+    if analysis_only:
+        st.markdown("### 🧠 Gemini Analysis Summary")
+        st.markdown(analysis_only)