.

.env

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+TEXT_ANALYTICS_KEY="d59c070ceefa417687e0b85ddf37a7c8"
+TEXT_ANALYTICS_ENDPOINT="https://lang097867575.cognitiveservices.azure.com/"
+AI_SERVICE_KEY= "af41c07c58894d25882d885b0954cf43"
+AI_SERVICE_ENDPOINT = "https://aimultiser979867857.cognitiveservices.azure.com/"
+TRANSLATOR_REGION="eastus"
+TRANSLATOR_KEY="e5a174e3fef04dbf8cae8199325a3df3"

app.py

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+from azure.ai.translation.text import TextTranslationClient, TranslatorCredential
+from azure.ai.translation.text.models import InputTextItem
+from azure.ai.textanalytics import TextAnalyticsClient
+from azure.ai.vision.imageanalysis import ImageAnalysisClient
+from azure.ai.vision.imageanalysis.models import VisualFeatures
+from azure.core.credentials import AzureKeyCredential
+from dotenv import load_dotenv
+import streamlit as st
+import os
+
+load_dotenv()
+
+# Initialize Azure service credentials
+text_analytics_endpoint = os.getenv('TEXT_ANALYTICS_ENDPOINT')
+text_analytics_key = os.getenv('TEXT_ANALYTICS_KEY')
+ai_endpoint = os.getenv('AI_SERVICE_ENDPOINT')
+ai_key = os.getenv('AI_SERVICE_KEY')
+translator_region = os.getenv('TRANSLATOR_REGION')
+translator_key = os.getenv('TRANSLATOR_KEY')
+
+# Function to analyze sentiment
+def analyze_sentiment(input_text):
+    client = TextAnalyticsClient(endpoint=text_analytics_endpoint, credential=AzureKeyCredential(text_analytics_key))
+    response = client.analyze_sentiment([input_text])
+    return response[0]
+
+# Function to analyze text input
+def analyze_text_input(inputText, target_language):
+    translator_client = TextTranslationClient(TranslatorCredential(translator_key, translator_region))
+    client = TextAnalyticsClient(endpoint=text_analytics_endpoint, credential=AzureKeyCredential(text_analytics_key))
+
+    sentiment_result = analyze_sentiment(inputText)
+    sentiment = sentiment_result.sentiment
+    confidence_scores = sentiment_result.confidence_scores
+
+    input_text_elements = [InputTextItem(text=inputText)]
+    translationResponse = translator_client.translate(content=input_text_elements, to=[target_language])
+    translation = translationResponse[0] if translationResponse else None
+
+    if translation:
+        sourceLanguage = translation.detected_language
+        translated_text = translation.translations[0].text
+        return {
+            "source_language": sourceLanguage,
+            "sentiment": sentiment,
+            "confidence_scores": confidence_scores,
+            "translated_text": translated_text
+        }
+    return None
+
+# Function to analyze image
+def analyze_image(image_file):
+    client = ImageAnalysisClient(endpoint=ai_endpoint, credential=AzureKeyCredential(ai_key))
+    with open(image_file, "rb") as f:
+        image_data = f.read()
+
+    result = client.analyze(image_data=image_data, visual_features=[VisualFeatures.READ])
+
+    detected_text = []
+    if result.read and result.read.blocks:
+        for block in result.read.blocks:
+            for line in block.lines:
+                detected_text.append(line.text)
+    return detected_text
+
+# Streamlit app
+def main():
+    st.title("Analyzer and Translator")
+
+    input_type = st.selectbox("Select input type:", ["Text", "Image"])
+
+    if input_type == "Text":
+        inputText = st.text_area("Enter text to analyze:")
+        target_language = st.text_input("Enter the target language code for translation (e.g., 'fr' for French):")
+
+        if st.button("Analyze Text"):
+            if inputText and target_language:
+                results = analyze_text_input(inputText, target_language)
+                if results:
+                    st.write(f"Detected language: {results['source_language']}")
+                    st.write(f"Sentiment: {results['sentiment']}")
+                    st.write(f"Confidence scores: Positive: {results['confidence_scores'].positive}, Neutral: {results['confidence_scores'].neutral}, Negative: {results['confidence_scores'].negative}")
+                    st.write(f"Translated text in {target_language}: {results['translated_text']}")
+                else:
+                    st.error("Error in translation.")
+
+    elif input_type == "Image":
+        uploaded_file = st.file_uploader("Choose an image...", type=["jpg", "jpeg", "png", "bmp"])
+
+        if uploaded_file is not None:
+            with open("temp_image", "wb") as f:
+                f.write(uploaded_file.getbuffer())
+            if st.button("Analyze Image"):
+                detected_text = analyze_image("temp_image")
+                if detected_text:
+                    st.write("Detected Text:")
+                    for line in detected_text:
+                        st.write(line)
+                else:
+                    st.write("No text detected in the image.")
+
+if __name__ == "__main__":
+    main()

main.py

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+from azure.ai.translation.text import TextTranslationClient, TranslatorCredential
+from azure.ai.translation.text.models import InputTextItem
+from azure.ai.textanalytics import TextAnalyticsClient
+from azure.ai.vision.imageanalysis import ImageAnalysisClient
+from azure.ai.vision.imageanalysis.models import VisualFeatures
+from azure.core.credentials import AzureKeyCredential
+from dotenv import load_dotenv
+import tkinter as tk
+from tkinter import filedialog
+import time
+import os
+
+load_dotenv()
+
+text_analytics_endpoint = os.getenv('TEXT_ANALYTICS_ENDPOINT')
+text_analytics_key = os.getenv('TEXT_ANALYTICS_KEY')
+ai_endpoint = os.getenv('AI_SERVICE_ENDPOINT')
+ai_key = os.getenv('AI_SERVICE_KEY')
+translator_region = os.getenv('TRANSLATOR_REGION')
+translator_key = os.getenv('TRANSLATOR_KEY')
+
+def analyze_sentiment(input_text):
+    client = TextAnalyticsClient(endpoint=text_analytics_endpoint, credential=AzureKeyCredential(text_analytics_key))
+    response = client.analyze_sentiment([input_text])
+    return response[0]
+
+def analyze_text_input(inputText, target_language):
+    translator_client = TextTranslationClient(TranslatorCredential(translator_key, translator_region))
+    client = TextAnalyticsClient(endpoint=text_analytics_endpoint, credential=AzureKeyCredential(text_analytics_key))
+
+    sentiment_result = analyze_sentiment(inputText)
+    sentiment = sentiment_result.sentiment
+    confidence_scores = sentiment_result.confidence_scores
+
+    input_text_elements = [InputTextItem(text=inputText)]
+    translationResponse = translator_client.translate(content=input_text_elements, to=[target_language])
+    translation = translationResponse[0] if translationResponse else None
+
+    if translation:
+        sourceLanguage = translation.detected_language
+        translated_text = translation.translations[0].text
+        return {
+            "source_language": sourceLanguage,
+            "sentiment": sentiment,
+            "confidence_scores": confidence_scores,
+            "translated_text": translated_text}
+    return None
+
+def get_image_file():
+    root = tk.Tk()
+    root.withdraw()
+    root.attributes("-topmost", True)
+    file_path = filedialog.askopenfilename(title="Select an Image", filetypes=[("Image Files", "*.jpg;*.jpeg;*.png;*.bmp")])
+    root.destroy()
+    return file_path
+
+def analyze_image(image_file):
+    client = ImageAnalysisClient(endpoint=ai_endpoint, credential=AzureKeyCredential(ai_key))
+    with open(image_file, "rb") as f:
+        image_data = f.read()
+
+    result = client.analyze(image_data=image_data, visual_features=[VisualFeatures.READ])
+
+    detected_text = []
+    if result.read and result.read.blocks:
+        for block in result.read.blocks:
+            for line in block.lines:
+                detected_text.append(line.text)
+    return detected_text
+
+def main():
+    while True:
+        os.system('cls' if os.name == 'nt' else 'clear')
+        input_type = input("What type of input do you want to analyze and translate?\n1. Text\n2. Image\nEnter your choice (1 or 2): ")
+        if input_type == '1':
+            inputText = input("Enter text to analyze: ")
+            target_language = input("Enter the target language code for translation (e.g., 'fr' for French): ")
+            results = analyze_text_input(inputText, target_language)
+            if results:
+                print(f"Detected language: {results['source_language']}")
+                print(f"Sentiment: {results['sentiment']}")
+                print(f"Confidence scores: Positive: {results['confidence_scores'].positive}, Neutral: {results['confidence_scores'].neutral}, Negative: {results['confidence_scores'].negative}")
+                print(f"Translated text in {target_language}: {results['translated_text']}")
+                time.sleep(5)
+            else:
+                print("Error in translation.")
+        elif input_type == '2':
+            image_file = get_image_file()
+            if image_file:
+                detected_text = analyze_image(image_file)
+                time.sleep(5)
+                if detected_text:
+                    print("Detected Text:")
+                    for line in detected_text:
+                        print(line)
+                else:
+                    print("No text detected in the image.")
+            else:
+                print("No file selected.")
+        else:
+            print("Invalid choice. Please enter 1 or 2.")
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+azure-ai-textanalytics==5.0.0
+azure-ai-translation-text==1.0.0b1
+azure-ai-vision-imageanalysis==1.0.0b3
+python-dotenv==1.0.0
+pytesseract==0.3.10
+requests==2.32.3
+speechrecognition==3.10.0
+pyttsx3==2.9
+streamlit==1.26.0