Upload 2 files

app.py

@@ -0,0 +1,460 @@
+# 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)

requirements.txt

@@ -1,3 +1,14 @@
-altair
-pandas
-streamlit
+streamlit
+torch
+torchaudio
+accelerate
+datasets
+transformers>=4.41.0
+moviepy==1.0.3
+pydub
+librosa
+google-generativeai
+streamlit-webrtc
+av
+soundfile
+audio-recorder-streamlit