Create app.py

app.py

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+import gradio as gr
+import os
+import whisper
+import torch
+from gtts import gTTS
+import IPython.display as ipd
+from sentence_transformers import SentenceTransformer
+import faiss
+import pandas as pd
+from datasets import load_dataset
+from deep_translator import GoogleTranslator
+from langdetect import detect
+from groq import Groq  # Correct import for Groq API
+
+# Set up Whisper with a smaller model or on CPU
+model_name = "small"  # Use "small", "base", or "medium" for smaller models
+whisper_model = whisper.load_model(model_name)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+whisper_model.to(device)
+
+# Initialize the GoogleTranslator from deep-translator
+translator = GoogleTranslator(source='auto', target='en')
+
+# Load and prepare the dataset for retrieval
+dataset = load_dataset("qgyd2021/e_commerce_customer_service", "faq")
+train_dataset = dataset['train']
+
+# Initialize the SentenceTransformer model
+embedder = SentenceTransformer('paraphrase-MiniLM-L6-v2')
+
+# Encode the questions from the dataset and set up FAISS
+dataset_embeddings = embedder.encode(train_dataset['question'], convert_to_tensor=True)
+index = faiss.IndexFlatL2(dataset_embeddings.shape[1])  # Create an index based on L2 distance
+index.add(dataset_embeddings.cpu().numpy())  # Add the embeddings to the index
+
+# Set up Groq API with direct API key
+
+client = Groq(api_key=api_key)
+import torch
+from transformers import pipeline
+from langdetect import detect
+from deep_translator import GoogleTranslator
+from gtts import gTTS
+
+# Initialize the sentiment analysis pipeline
+device = 0 if torch.cuda.is_available() else -1
+try:
+    sentiment_analyzer = pipeline("sentiment-analysis", model="distilbert-base-uncased-finetuned-sst-2-english", device=device)
+except Exception as e:
+    print(f"Error loading sentiment analysis model: {e}")
+
+# Function to detect the language
+def detect_language(text):
+    try:
+        return detect(text)
+    except Exception as e:
+        print(f"Error during language detection: {e}")
+        return "en"  # Default to English if detection fails
+
+# Translate text using deep-translator
+def translate_text(text, dest_lang):
+    try:
+        return GoogleTranslator(source='auto', target=dest_lang).translate(text)
+    except Exception as e:
+        print(f"Error during translation: {e}")
+        return text  # Return original text if translation fails
+
+# Function to generate a greeting based on sentiment
+def generate_greeting(sentiment, lang):
+    try:
+        if sentiment == 'NEGATIVE':
+            if lang in ['ur', 'hi']:
+                return "پریشان نہ ہوں، میں آپ کی مدد کے لئے یہاں ہوں."
+            else:
+                return "Please don't be sad, I'm here to solve your problem."
+        elif sentiment == 'NEUTRAL':
+            if lang in ['ur', 'hi']:
+                return "آپ کا مسئلہ حل کرتے ہیں، آپ فکر نہ کریں."
+            else:
+                return "I understand your concern, let's get that sorted out."
+        elif sentiment == 'POSITIVE':
+            if lang in ['ur', 'hi']:
+                return "یہ خوشی کی بات ہے کہ آپ خوش ہیں! آئیں، ہم اسے بہتر بناتے ہیں."
+            else:
+                return "I'm glad you're feeling positive! Let's make things even better."
+        else:
+            if lang in ['ur', 'hi']:
+                return "ہیلو! میں آج تمہاری مدد کیسے کر سکتا ہوں؟"
+            else:
+                return "Hello! How can I assist you today?"
+    except Exception as e:
+        print(f"Error generating greeting: {e}")
+        return "Hello!"
+
+# Function to transcribe audio using Whisper
+def transcribe_audio(audio_path):
+    try:
+        result = whisper_model.transcribe(audio_path)
+        transcription = result['text']
+        print(f"Transcription result: {transcription}")
+        return transcription
+    except Exception as e:
+        print(f"Error during transcription: {e}")
+        return "Error during transcription"
+
+# Function to generate a chatbot response based on transcription
+def generate_chatbot_response(transcription):
+    try:
+        # Detect language of the transcription
+        detected_language = detect_language(transcription)
+
+        # Translate to English if necessary
+        if detected_language in ['ur', 'hi']:
+            transcription = translate_text(transcription, 'en')
+
+        # Perform sentiment analysis
+        sentiment_result = sentiment_analyzer(transcription)[0]
+        sentiment = sentiment_result['label'].upper()
+
+        # Generate a greeting based on sentiment
+        greeting = generate_greeting(sentiment, detected_language)
+
+        # Retrieve relevant context using FAISS
+        transcription_embedding = embedder.encode([transcription], convert_to_tensor=True)
+        _, indices = index.search(transcription_embedding.cpu().numpy(), k=1)
+        best_match_index = indices[0][0]
+        context = train_dataset['answer'][best_match_index]
+        url = train_dataset['url'][best_match_index]
+
+        # Generate the full response
+        response = f"{greeting}\n\n{context}\n\nPlease visit this link for your query: {url}"
+
+        # Translate the response back to Urdu if necessary
+        if detected_language in ['ur', 'hi']:
+            response = translate_text(response, 'ur')
+
+        return response
+    except Exception as e:
+        print(f"Error during chatbot response generation: {e}")
+        return "Error during response generation"
+
+# Function to convert text to speech using gTTS
+def text_to_speech(text, lang='en'):
+    try:
+        tts = gTTS(text=text, lang=lang)
+        tts.save("response.mp3")
+        return "response.mp3"
+    except Exception as e:
+        print(f"Error during text-to-speech conversion: {e}")
+        return "Error during text-to-speech conversion"
+
+# Main function for Gradio interface
+def chatbot(text_input=None, audio_input=None):
+    if audio_input:
+        # Step 1: Transcribe audio to text if audio input is provided
+        transcription = transcribe_audio(audio_input)
+        input_text = transcription
+    else:
+        # Use the text input directly if provided
+        input_text = text_input
+
+    # Step 2: Generate a chatbot response based on the input text
+    response = generate_chatbot_response(input_text)
+
+    # Step 3: Convert the response text to speech if the original input was audio
+    if audio_input:
+        lang = 'ur' if detect_language(input_text) in ['ur', 'hi'] else 'en'
+        audio_path = text_to_speech(response, lang=lang)
+        return input_text, response, audio_path
+    else:
+        return input_text, response, None
+
+# Custom CSS for styling the interface and buttons
+custom_css = """
+body {
+    font-family: 'Arial', sans-serif;
+    background-color: #1e1e1e;  /* Black background */
+    color: white;  /* White text */
+}
+
+h1 {
+    font-size: 36px;
+    color: white;
+    text-align: center;
+    margin-bottom: 20px;
+}
+
+h2 {
+    font-size: 24px;
+    color: white;
+    text-align: center;
+    margin-bottom: 10px;
+}
+
+.instructions {
+    font-size: 16px;  /* Smaller font size for instructions */
+    color: #cccccc;  /* Light gray color for instructions */
+    text-align: center;
+    margin-bottom: 20px;
+}
+
+.gradio-container {
+    background-color: #1e1e1e;
+    padding: 20px;
+    border-radius: 10px;
+}
+
+.gr-box {
+    border-radius: 5px;
+    border: 1px solid #333;
+    padding: 10px;
+    margin-bottom: 10px;
+}
+
+.gr-button {
+    border-radius: 5px;
+    padding: 10px;
+    font-weight: bold;
+    font-size: 16px;
+    transition: background-color 0.3s;
+}
+
+.gr-button-submit {
+    background-color: #28a745;  /* Green submit button */
+    color: white;
+}
+
+.gr-button-submit:hover {
+    background-color: #218838;
+}
+
+.gr-button-clear {
+    background-color: #dc3545;  /* Red clear button */
+    color: white;
+}
+
+.gr-button-clear:hover {
+    background-color: #c82333;
+}
+
+.gr-textbox, .gr-audio {
+    border-radius: 5px;
+    border: 1px solid #0056b3;  /* Blue border */
+    padding: 8px;
+    background-color: #2e2e2e;
+    color: white;
+}
+
+.gr-textbox {
+    background-color: #0056b3;  /* Blue background for textboxes */
+    color: white;
+}
+
+.gr-container {
+    max-width: 900px;
+    margin: auto;
+}
+"""
+
+import gradio as gr
+
+# Gradio interface setup with updated CSS
+with gr.Blocks(css=custom_css) as iface:
+    gr.Markdown("<h1>Multilingual Customer Service Chatbot</h1>")
+    gr.Markdown("<h2>Ask your questions</h2>")
+    gr.Markdown("<p class='instructions'>If you type in Urdu, it will respond in Urdu. If in English, it will respond in English. Same with voice.</p>")
+    
+    with gr.Row():
+        with gr.Column():
+            text_input = gr.Textbox(lines=2, placeholder="Type your query here...", label="Text Input (Optional)")
+            audio_input = gr.Audio(type="filepath", label="Audio Input (Optional)")
+        with gr.Column():
+            transcription_output = gr.Textbox(label="Transcription")  # Add transcription output
+            response_text = gr.Textbox(label="Chatbot Response")
+            response_audio = gr.Audio(label="Response Audio (if applicable)")
+    
+    with gr.Row():
+        submit_btn = gr.Button("Submit", elem_id="submit-btn", variant="primary")
+        clear_btn = gr.Button("Clear", elem_id="clear-btn", variant="secondary")
+    
+    submit_btn.click(chatbot, inputs=[text_input, audio_input], outputs=[transcription_output, response_text, response_audio])
+    clear_btn.click(lambda: (None, None, None, None, None), inputs=[], outputs=[text_input, audio_input, transcription_output, response_text, response_audio])
+
+# Launch the Gradio interface
+iface.launch()