app.py

# import streamlit as st
# from transformers import AutoProcessor, Wav2Vec2ForCTC
# import torch
# import librosa
# import os
# from pydub import AudioSegment
# from moviepy.editor import VideoFileClip
# import google.generativeai as genai
# 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=st.secrets["GEMINI_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
    
# # def analyze_transcript(transcript: str):
# #     client = genai.Client(api_key=st.secrets["GEMINI_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

# # ----------- 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)


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)