Create app.py

app.py

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+import gradio as gr
+from langchain_groq import ChatGroq
+from langchain.schema import SystemMessage, HumanMessage
+import requests
+import tempfile
+import time
+
+# Configuration of the Groq model
+groq_api_key = "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"  # Replace with your actual Groq API key
+llm = ChatGroq(api_key=groq_api_key, model_name="llama3-70b-8192")  # Corrected model name
+
+# ElevenLabs API key and voice ID
+XI_API_KEY = "sk_b254c267851485b60d23fb2e15fa8fde9f5fbc0d835127e2"  # Replace with your ElevenLabs API key
+VOICE_ID = "iYwRDEf2D1WyqRRecXPA"  # Replace with your voice ID
+
+def translate_and_speak(user_input, target_language):
+    try:
+        start_time = time.time()  # Start total processing time
+
+        # Generate translation using Groq model
+        translation_start = time.time()
+        system_prompt = f"You are expected to translate the user input exclusively into {target_language} without adding anything else."
+        messages = [
+            SystemMessage(content=system_prompt),
+            HumanMessage(content=user_input)
+        ]
+        response = llm.invoke(messages)
+        translation_end = time.time()
+
+        # Check if the response is valid
+        if not response or not hasattr(response, 'content'):
+            raise ValueError("Invalid response from the translation model.")
+
+        generated_text = response.content.strip()
+
+        # Use ElevenLabs API to generate speech
+        tts_start = time.time()
+        url = f"https://api.elevenlabs.io/v1/text-to-speech/{VOICE_ID}"
+        headers = {
+            "Accept": "audio/mpeg",
+            "Content-Type": "application/json",
+            "xi-api-key": XI_API_KEY
+        }
+        data = {
+            "text": generated_text,
+            "model_id": "eleven_multilingual_v2",
+            "voice_settings": {
+                "stability": 0.75,
+                "similarity_boost": 0.75
+            }
+        }
+        tts_response = requests.post(url, json=data, headers=headers)
+        tts_end = time.time()
+
+        if tts_response.status_code == 200:
+            # Save audio to a temporary file
+            with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as fp:
+                fp.write(tts_response.content)
+                audio_file = fp.name
+            end_time = time.time()
+
+            # Calculate processing times
+            translation_time = translation_end - translation_start
+            tts_time = tts_end - tts_start
+            total_time = end_time - start_time
+
+            # Prepare timings information
+            timings_info = f"Translation time: {translation_time:.2f} seconds\n"
+            timings_info += f"Text-to-Speech time: {tts_time:.2f} seconds\n"
+            timings_info += f"Total processing time: {total_time:.2f} seconds"
+
+            return generated_text, audio_file, timings_info
+        else:
+            error_message = f"Text-to-Speech API Error: {tts_response.status_code} - {tts_response.text}"
+            return error_message, None, None
+    except Exception as e:
+        # Return the exception message
+        error_details = f"An error occurred: {str(e)}"
+        return error_details, None, None
+
+# Create Gradio interface
+iface = gr.Interface(
+    fn=translate_and_speak,
+    inputs=[
+        gr.Textbox(lines=2, placeholder="Enter text to translate...", label="Input Text"),
+        gr.Dropdown(
+            choices=["Spanish", "French", "German", "Italian", "Chinese", "Japanese"],
+            value="Spanish",
+            label="Target Language"
+        )
+    ],
+    outputs=[
+        gr.Textbox(label="Translated Text"),
+        gr.Audio(label="Spoken Audio", autoplay=True),
+        gr.Textbox(label="Processing Times")
+    ],
+    title="Multilingual Text Translator and Speech Synthesizer",
+    description="Translate text into the selected language and listen to the spoken audio.",
+    allow_flagging="never"
+)
+
+# Launch the app
+iface.launch()