Update app.py

app.py

@@ -3,31 +3,47 @@ import gradio as gr
 import whisper
 from gtts import gTTS
 import io
-import almlapi  # Assuming AL/ML has a Python API library
+from openai import OpenAI  # Import OpenAI for AI/ML API calls
 
-# Set your AL/ML API key for authentication
-os.environ["ALML_API_KEY"] = "701b35863e6d4a7b81bdcad2e6f3c880"
+# Set the base URL and API key for AI/ML API
+base_url = "https://api.aimlapi.com/v1"
+api_key = os.getenv("701b35863e6d4a7b81bdcad2e6f3c880")  # Make sure to set this environment variable
+
+# Initialize the OpenAI API with the custom base URL
+api = OpenAI(api_key=api_key, base_url=base_url)
 
 # Load the Whisper model for audio transcription
 model = whisper.load_model("base")
 
-# Function to process audio and interact with the AL/ML API
+# Function to make a chat completion call to the AI/ML API
+def call_aiml_api(user_prompt, system_prompt="You are a helpful assistant."):
+    try:
+        completion = api.chat.completions.create(
+            model="mistralai/Mistral-7B-Instruct-v0.2",  # Specify the model from AI/ML
+            messages=[
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": user_prompt},
+            ],
+            temperature=0.7,
+            max_tokens=256,
+        )
+
+        # Return the response from the AI model
+        return completion.choices[0].message.content.strip()
+
+    except Exception as e:
+        raise Exception(f"API request failed with error: {e}")
+
+# Function to process audio and interact with the AI/ML API
 def process_audio(file_path):
     try:
         # Load and transcribe audio using Whisper
         audio = whisper.load_audio(file_path)
         result = model.transcribe(audio)
-        text = result["text"]
-
-        # Call OpenAI o1 model via AL/ML API for problem-solving
-        response = almlapi.call_o1(
-            api_key=os.environ.get("ALML_API_KEY"),
-            prompt=text,
-            model="o1"  # Model name, adjust according to AL/ML documentation
-        )
+        user_prompt = result["text"]
 
-        # Extract the response message
-        response_message = response["generated_text"].strip()
+        # Call AI/ML API to get a response
+        response_message = call_aiml_api(user_prompt)
 
         # Convert response message to speech using gTTS
         tts = gTTS(response_message)
@@ -43,11 +59,11 @@ def process_audio(file_path):
         return response_message, "response.mp3"
 
     except Exception as e:
-        # Handle exceptions
+        # Handle any errors
         return f"An error occurred: {e}", None
 
 # Interface configurations (UI)
-title = "Voice-to-Voice AI Chatbot with AL/ML API"
+title = "Voice-to-Voice AI Chatbot with AI/ML API"
 description = "Developed by [Adnan Tariq](https://www.linkedin.com/in/adnaantariq/) with ❤️"
 article = "### Instructions\n1. Upload an audio file.\n2. Wait for the transcription.\n3. Listen to the chatbot's response."