Rename streamlit_main.py to app.py

streamlit_main.py → app.py

@@ -1,206 +1,206 @@
-import streamlit as st
-from PIL import Image
-import json
-import os
-import tempfile
-
-from Lead_score_conversion import (
-    MalayalamTranscriptionPipeline,
-    analyze_text,
-    save_analysis_to_csv,
-    compare_analyses,
-    print_analysis_summary
-)
-
-# Function to load Lottie or fallback image
-def load_lottie(filepath):
-    if os.path.exists(filepath):
-        with open(filepath, "r") as f:
-            return json.load(f)
-    return None
-
-def display_lottie_or_image(lottie_data, fallback_image_path, height=200, key=None):
-    try:
-        from streamlit_lottie import st_lottie
-        if lottie_data:
-            st_lottie(lottie_data, height=height, key=key)
-            return
-    except ImportError:
-        pass
-
-    if os.path.exists(fallback_image_path):
-        img = Image.open(fallback_image_path)
-        st.image(img, width=height)
-    else:
-        st.warning("Animation and fallback image not found.")
-
-# Load animations or fallback
-upload_anim = load_lottie("animations/upload.json")
-analyze_anim = load_lottie("animations/analyze.json")
-results_anim = load_lottie("animations/results.json")
-
-# Page config
-st.set_page_config(
-    page_title="Malayalam Audio Analyzer",
-    page_icon="🎤",
-    layout="wide",
-    initial_sidebar_state="expanded"
-)
-
-# Custom CSS
-st.markdown("""
-<style>
-    .stProgress > div > div > div > div {
-        background-image: linear-gradient(to right, #00ccff, #00ffaa);
-    }
-    .stButton>button {
-        border-radius: 20px;
-        font-weight: bold;
-    }
-    .highlight-box {
-        border-radius: 15px;
-        padding: 1rem;
-        margin: 1rem 0;
-        background: rgba(0, 204, 255, 0.1);
-        border-left: 5px solid #00ccff;
-    }
-    .metric-card {
-        border-radius: 10px;
-        padding: 1.5rem;
-        margin: 0.5rem;
-        background: rgba(255, 255, 255, 0.1);
-        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
-        transition: transform 0.3s;
-    }
-    .metric-card:hover {
-        transform: translateY(-5px);
-    }
-</style>
-""", unsafe_allow_html=True)
-
-# Title
-st.title("🎙️ Malayalam Audio Intelligence Analyzer")
-st.markdown("""
-Upload a Malayalam audio file to extract insights, analyze sentiment and intent, and generate lead scores.
-""")
-
-# Sidebar info
-with st.sidebar:
-    st.header("ℹ️ About")
-    st.markdown("""
-    This tool analyzes Malayalam audio to:
-    - Transcribe speech to text
-    - Translate to English
-    - Detect sentiment & intent
-    - Calculate lead scores
-    """)
-    st.header("📁 Supported Formats")
-    st.markdown("MP3, WAV, AAC, M4A, FLAC, OGG, WMA")
-
-# File Upload
-with st.container():
-    col1, col2 = st.columns([3, 1])
-    with col1:
-        uploaded_file = st.file_uploader("Upload Malayalam audio file", type=[
-            "mp3", "wav", "aac", "m4a", "flac", "ogg", "wma"
-        ])
-    with col2:
-        display_lottie_or_image(upload_anim, "images/upload.png", height=150, key="upload")
-
-if uploaded_file is not None:
-    with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(uploaded_file.name)[1]) as tmp:
-        tmp.write(uploaded_file.read())
-        tmp_path = tmp.name
-
-    transcriber = MalayalamTranscriptionPipeline()
-
-    try:
-        with st.container():
-            st.subheader("🔍 Processing Pipeline")
-            col1, col2 = st.columns([3, 1])
-            with col1:
-                progress_bar = st.progress(0)
-                status_text = st.empty()
-
-                status_text.markdown("### 🎧 Transcribing audio...")
-                progress_bar.progress(20)
-                results = transcriber.transcribe_audio(tmp_path)
-                progress_bar.progress(40)
-
-                if not results or not results.get("raw_transcription"):
-                    st.error("Transcription failed.")
-                else:
-                    raw_text = results["raw_transcription"]
-
-                    status_text.markdown("### 🌐 Translating to English...")
-                    progress_bar.progress(60)
-                    translated = transcriber.translate_to_malayalam(results)
-                    ml_text = translated.get("malayalam_translation", "")
-                    progress_bar.progress(80)
-
-                    status_text.markdown("### 📊 Analyzing content...")
-                    en_analysis = analyze_text(raw_text, "en")
-                    ml_analysis = analyze_text(ml_text, "ml")
-                    comparison = compare_analyses(en_analysis, ml_analysis)
-                    progress_bar.progress(100)
-
-                    status_text.success("✅ Analysis completed!")
-            with col2:
-                display_lottie_or_image(analyze_anim, "images/analyze.png", height=200, key="analyze")
-
-        st.subheader("📋 Results Overview")
-        tab1, tab2 = st.tabs(["English Transcription", "Malayalam Translation"])
-        with tab1:
-            st.markdown(f'<div class="highlight-box">{raw_text}</div>', unsafe_allow_html=True)
-        with tab2:
-            st.markdown(f'<div class="highlight-box">{ml_text}</div>', unsafe_allow_html=True)
-
-        st.subheader("📊 Key Metrics")
-        col1, col2, col3 = st.columns(3)
-        with col1:
-            st.markdown('<div class="metric-card"><h3>Sentiment Match</h3><p style="font-size: 2rem;">✅ 85%</p></div>', unsafe_allow_html=True)
-        with col2:
-            st.markdown('<div class="metric-card"><h3>Intent Match</h3><p style="font-size: 2rem;">🎯 78%</p></div>', unsafe_allow_html=True)
-        with col3:
-            en_avg = sum(x["sentiment_score"] for x in en_analysis) / len(en_analysis) if en_analysis else 0
-            ml_avg = sum(x["sentiment_score"] for x in ml_analysis) / len(ml_analysis) if ml_analysis else 0
-            lead_score = int(((en_avg + ml_avg) / 2) * 100)
-            score_color = "#00ff88" if lead_score >= 70 else "#ffaa00" if lead_score >= 40 else "#ff5555"
-            st.markdown(f'<div class="metric-card"><h3>Lead Score</h3><p style="font-size: 2rem; color: {score_color}">🔥 {lead_score}/100</p></div>', unsafe_allow_html=True)
-
-        with st.expander("🔍 Detailed Sentiment Analysis"):
-            col1, col2 = st.columns(2)
-            with col1:
-                st.markdown("#### 🇬🇧 English Analysis")
-                print_analysis_summary(en_analysis, "English")
-            with col2:
-                st.markdown("#### 🇮🇳 Malayalam Analysis")
-                print_analysis_summary(ml_analysis, "Malayalam")
-
-        st.subheader("📥 Download Results")
-        col1, col2, col3 = st.columns(3)
-        en_csv = save_analysis_to_csv(en_analysis, "english")
-        ml_csv = save_analysis_to_csv(ml_analysis, "malayalam")
-        comparison_csv = save_analysis_to_csv(comparison, "comparison")
-
-        with col1:
-            if en_csv and os.path.exists(en_csv):
-                with open(en_csv, "rb") as f:
-                    st.download_button("⬇️ English Analysis", f.read(), file_name=os.path.basename(en_csv), mime="text/csv")
-        with col2:
-            if ml_csv and os.path.exists(ml_csv):
-                with open(ml_csv, "rb") as f:
-                    st.download_button("⬇️ Malayalam Analysis", f.read(), file_name=os.path.basename(ml_csv), mime="text/csv")
-        with col3:
-            if comparison_csv and os.path.exists(comparison_csv):
-                with open(comparison_csv, "rb") as f:
-                    st.download_button("⬇️ Comparison Report", f.read(), file_name=os.path.basename(comparison_csv), mime="text/csv")
-
-        display_lottie_or_image(results_anim, "images/results.png", height=300, key="results")
-
-    except Exception as e:
-        st.error(f"❌ Error: {str(e)}")
-        st.exception(e)
-    finally:
-        transcriber.cleanup()
-        os.remove(tmp_path)
+import streamlit as st
+from PIL import Image
+import json
+import os
+import tempfile
+
+from streamlit_app import (
+    MalayalamTranscriptionPipeline,
+    analyze_text,
+    save_analysis_to_csv,
+    compare_analyses,
+    print_analysis_summary
+)
+
+# Function to load Lottie or fallback image
+def load_lottie(filepath):
+    if os.path.exists(filepath):
+        with open(filepath, "r") as f:
+            return json.load(f)
+    return None
+
+def display_lottie_or_image(lottie_data, fallback_image_path, height=200, key=None):
+    try:
+        from streamlit_lottie import st_lottie
+        if lottie_data:
+            st_lottie(lottie_data, height=height, key=key)
+            return
+    except ImportError:
+        pass
+
+    if os.path.exists(fallback_image_path):
+        img = Image.open(fallback_image_path)
+        st.image(img, width=height)
+    else:
+        st.warning("Animation and fallback image not found.")
+
+# Load animations or fallback
+upload_anim = load_lottie("animations/upload.json")
+analyze_anim = load_lottie("animations/analyze.json")
+results_anim = load_lottie("animations/results.json")
+
+# Page config
+st.set_page_config(
+    page_title="Malayalam Audio Analyzer",
+    page_icon="🎤",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Custom CSS
+st.markdown("""
+<style>
+    .stProgress > div > div > div > div {
+        background-image: linear-gradient(to right, #00ccff, #00ffaa);
+    }
+    .stButton>button {
+        border-radius: 20px;
+        font-weight: bold;
+    }
+    .highlight-box {
+        border-radius: 15px;
+        padding: 1rem;
+        margin: 1rem 0;
+        background: rgba(0, 204, 255, 0.1);
+        border-left: 5px solid #00ccff;
+    }
+    .metric-card {
+        border-radius: 10px;
+        padding: 1.5rem;
+        margin: 0.5rem;
+        background: rgba(255, 255, 255, 0.1);
+        box-shadow: 0 4px 6px rgba(0, 0, 0, 0.1);
+        transition: transform 0.3s;
+    }
+    .metric-card:hover {
+        transform: translateY(-5px);
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Title
+st.title("🎙️ Malayalam Audio Intelligence Analyzer")
+st.markdown("""
+Upload a Malayalam audio file to extract insights, analyze sentiment and intent, and generate lead scores.
+""")
+
+# Sidebar info
+with st.sidebar:
+    st.header("ℹ️ About")
+    st.markdown("""
+    This tool analyzes Malayalam audio to:
+    - Transcribe speech to text
+    - Translate to English
+    - Detect sentiment & intent
+    - Calculate lead scores
+    """)
+    st.header("📁 Supported Formats")
+    st.markdown("MP3, WAV, AAC, M4A, FLAC, OGG, WMA")
+
+# File Upload
+with st.container():
+    col1, col2 = st.columns([3, 1])
+    with col1:
+        uploaded_file = st.file_uploader("Upload Malayalam audio file", type=[
+            "mp3", "wav", "aac", "m4a", "flac", "ogg", "wma"
+        ])
+    with col2:
+        display_lottie_or_image(upload_anim, "images/upload.png", height=150, key="upload")
+
+if uploaded_file is not None:
+    with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(uploaded_file.name)[1]) as tmp:
+        tmp.write(uploaded_file.read())
+        tmp_path = tmp.name
+
+    transcriber = MalayalamTranscriptionPipeline()
+
+    try:
+        with st.container():
+            st.subheader("🔍 Processing Pipeline")
+            col1, col2 = st.columns([3, 1])
+            with col1:
+                progress_bar = st.progress(0)
+                status_text = st.empty()
+
+                status_text.markdown("### 🎧 Transcribing audio...")
+                progress_bar.progress(20)
+                results = transcriber.transcribe_audio(tmp_path)
+                progress_bar.progress(40)
+
+                if not results or not results.get("raw_transcription"):
+                    st.error("Transcription failed.")
+                else:
+                    raw_text = results["raw_transcription"]
+
+                    status_text.markdown("### 🌐 Translating to English...")
+                    progress_bar.progress(60)
+                    translated = transcriber.translate_to_malayalam(results)
+                    ml_text = translated.get("malayalam_translation", "")
+                    progress_bar.progress(80)
+
+                    status_text.markdown("### 📊 Analyzing content...")
+                    en_analysis = analyze_text(raw_text, "en")
+                    ml_analysis = analyze_text(ml_text, "ml")
+                    comparison = compare_analyses(en_analysis, ml_analysis)
+                    progress_bar.progress(100)
+
+                    status_text.success("✅ Analysis completed!")
+            with col2:
+                display_lottie_or_image(analyze_anim, "images/analyze.png", height=200, key="analyze")
+
+        st.subheader("📋 Results Overview")
+        tab1, tab2 = st.tabs(["English Transcription", "Malayalam Translation"])
+        with tab1:
+            st.markdown(f'<div class="highlight-box">{raw_text}</div>', unsafe_allow_html=True)
+        with tab2:
+            st.markdown(f'<div class="highlight-box">{ml_text}</div>', unsafe_allow_html=True)
+
+        st.subheader("📊 Key Metrics")
+        col1, col2, col3 = st.columns(3)
+        with col1:
+            st.markdown('<div class="metric-card"><h3>Sentiment Match</h3><p style="font-size: 2rem;">✅ 85%</p></div>', unsafe_allow_html=True)
+        with col2:
+            st.markdown('<div class="metric-card"><h3>Intent Match</h3><p style="font-size: 2rem;">🎯 78%</p></div>', unsafe_allow_html=True)
+        with col3:
+            en_avg = sum(x["sentiment_score"] for x in en_analysis) / len(en_analysis) if en_analysis else 0
+            ml_avg = sum(x["sentiment_score"] for x in ml_analysis) / len(ml_analysis) if ml_analysis else 0
+            lead_score = int(((en_avg + ml_avg) / 2) * 100)
+            score_color = "#00ff88" if lead_score >= 70 else "#ffaa00" if lead_score >= 40 else "#ff5555"
+            st.markdown(f'<div class="metric-card"><h3>Lead Score</h3><p style="font-size: 2rem; color: {score_color}">🔥 {lead_score}/100</p></div>', unsafe_allow_html=True)
+
+        with st.expander("🔍 Detailed Sentiment Analysis"):
+            col1, col2 = st.columns(2)
+            with col1:
+                st.markdown("#### 🇬🇧 English Analysis")
+                print_analysis_summary(en_analysis, "English")
+            with col2:
+                st.markdown("#### 🇮🇳 Malayalam Analysis")
+                print_analysis_summary(ml_analysis, "Malayalam")
+
+        st.subheader("📥 Download Results")
+        col1, col2, col3 = st.columns(3)
+        en_csv = save_analysis_to_csv(en_analysis, "english")
+        ml_csv = save_analysis_to_csv(ml_analysis, "malayalam")
+        comparison_csv = save_analysis_to_csv(comparison, "comparison")
+
+        with col1:
+            if en_csv and os.path.exists(en_csv):
+                with open(en_csv, "rb") as f:
+                    st.download_button("⬇️ English Analysis", f.read(), file_name=os.path.basename(en_csv), mime="text/csv")
+        with col2:
+            if ml_csv and os.path.exists(ml_csv):
+                with open(ml_csv, "rb") as f:
+                    st.download_button("⬇️ Malayalam Analysis", f.read(), file_name=os.path.basename(ml_csv), mime="text/csv")
+        with col3:
+            if comparison_csv and os.path.exists(comparison_csv):
+                with open(comparison_csv, "rb") as f:
+                    st.download_button("⬇️ Comparison Report", f.read(), file_name=os.path.basename(comparison_csv), mime="text/csv")
+
+        display_lottie_or_image(results_anim, "images/results.png", height=300, key="results")
+
+    except Exception as e:
+        st.error(f"❌ Error: {str(e)}")
+        st.exception(e)
+    finally:
+        transcriber.cleanup()
+        os.remove(tmp_path)