Update app.py

app.py

@@ -1,46 +1,82 @@
 import streamlit as st
-from ftplib import FTP, error_perm, error_temp
-
-# FTP server credentials
-host = "cph.v4one.co.uk"
-username = "yash.sharma"
-password = "8x]EnG/Z%DXvcjt[%<;S"  # Replace with your actual password
-
-# Streamlit UI for FTP connection
-st.title("FTP File Browser")
-
-st.sidebar.header("FTP Connection Details")
-host_input = st.sidebar.text_input("Host", host)
-username_input = st.sidebar.text_input("Username", username)
-password_input = st.sidebar.text_input("Password", type="password", value=password)
-
-if st.sidebar.button("🔄 Connect & List Files"):
-    try:
-        # Connect to FTP server with a timeout of 120 seconds
-        
-        ftp = FTP(host_input, timeout=120)
-        ftp.connect(host, 21)
-        ftp.login(user=username_input, passwd=password_input)
-
-        # Enable passive mode
-        ftp.set_pasv(True)
-
-        # List files in the root directory
-        files = []
-        ftp.retrlines('LIST', lambda x: files.append(x.split()[-1]))  # Get filenames from LIST command
-
-        ftp.quit()
-
-        # Show files in dropdown
-        if files:
-            selected_file = st.selectbox("Select a file", files)
-            st.write(f"File selected: {selected_file}")
-        else:
-            st.warning("No files found on the FTP server.")
-
-    except error_perm as e:
-        st.error(f"FTP Permission Error: {e}")
-    except error_temp as e:
-        st.error(f"FTP Temporary Error: {e}")
-    except Exception as e:
-        st.error(f"Error: {e}")
+import librosa
+import numpy as np
+import matplotlib.pyplot as plt
+from pydub import AudioSegment
+from transformers import pipeline
+import os
+
+# Load pre-trained sentiment analysis model
+sentiment_analyzer = pipeline("sentiment-analysis")
+
+# Streamlit UI
+st.title("🎤 Audio Sentiment Analysis")
+st.write("Upload multiple MP3 files to analyze sentiment and tone.")
+
+# Upload multiple audio files
+uploaded_files = st.file_uploader("Choose MP3 files", type=["mp3"], accept_multiple_files=True)
+
+# Function to process audio and get sentiment
+def analyze_audio(file_path):
+    # Convert MP3 to WAV
+    audio = AudioSegment.from_mp3(file_path)
+    wav_path = file_path.replace(".mp3", ".wav")
+    audio.export(wav_path, format="wav")
+
+    # Load audio
+    y, sr = librosa.load(wav_path, sr=None)
+    
+    # Extract MFCCs (Mel-frequency cepstral coefficients)
+    mfccs = librosa.feature.mfcc(y=y, sr=sr, n_mfcc=13)
+    mfccs_mean = np.mean(mfccs, axis=1)  # Take mean across time axis
+
+    # Dummy text for sentiment analysis (replace with actual text from speech-to-text if needed)
+    sentiment_result = sentiment_analyzer("This is a placeholder for sentiment analysis based on audio!")
+
+    # Remove WAV file after processing
+    os.remove(wav_path)
+    
+    return sentiment_result[0], mfccs_mean, y, sr
+
+# Process and plot if files are uploaded
+if uploaded_files:
+    # Create a directory to store temporary files
+    os.makedirs("temp", exist_ok=True)
+
+    for uploaded_file in uploaded_files:
+        # Save the uploaded file
+        file_path = f"temp/{uploaded_file.name}"
+        with open(file_path, "wb") as f:
+            f.write(uploaded_file.getbuffer())
+
+        # Analyze sentiment and extract features
+        sentiment, mfccs, audio_data, sample_rate = analyze_audio(file_path)
+
+        # Display sentiment result
+        st.subheader(f"📊 Sentiment Analysis Result for {uploaded_file.name}")
+        st.write(f"**Sentiment:** {sentiment['label']}")
+        st.write(f"**Confidence:** {sentiment['score']:.2f}")
+
+        # Agent Tone Plot (Sentiment over time)
+        st.subheader(f"🎵 Agent Tone Plot for {uploaded_file.name}")
+        tone_score = sentiment['score'] if sentiment['label'] == 'POSITIVE' else -sentiment['score']
+        fig, ax = plt.subplots()
+        ax.plot([tone_score] * len(audio_data), label="Agent's Tone")
+        ax.set_xlabel("Time (seconds)")
+        ax.set_ylabel("Tone Score")
+        ax.set_title("Agent's Tone Plot Over Time")
+        ax.legend()
+        st.pyplot(fig)
+
+        # Patient Tone Curve (MFCC features)
+        st.subheader(f"🎶 Patient Tone Curve for {uploaded_file.name}")
+        fig, ax = plt.subplots()
+        ax.plot(np.mean(mfccs, axis=0), label="Patient's Tone Curve (MFCCs)")
+        ax.set_xlabel("MFCC Coefficients")
+        ax.set_ylabel("Mean Value")
+        ax.set_title("Patient Tone Curve")
+        ax.legend()
+        st.pyplot(fig)
+
+        # Clean up temp file
+        os.remove(file_path)