Update app.py

app.py

@@ -6,50 +6,64 @@ from transformers import pipeline
 import torch
 from huggingface_hub import InferenceClient
 import os
+import librosa
 
 # Initialize the InferenceClient for PHI 3
 client = InferenceClient(
     "microsoft/Phi-3.5-mini-instruct",   # Update this to the correct model name for PHI 3
     token=os.getenv("HF_API_TOKEN", "")
-     # You can configure this API token through the Hugging Face Secrets
 )
 
 # Check if a GPU is available and use it if possible
 device = 'cuda' if torch.cuda.is_available() else 'cpu'
 
 # Initialize the Whisper pipeline
-whisper = pipeline('automatic-speech-recognition', model='openai/whisper-tiny', device=0 if device == 'cuda' else -1)
+whisper = pipeline('automatic-speech-recognition', model='openai/whisper-tiny', device=device)
 
 # Instructions (can be set through Hugging Face Secrets or hardcoded)
 instructions = os.getenv("INST", "Your default instructions here.")
 
 def query_phi(prompt):
-    response = ""  # Initialize an empty string to store the response
-    for message in client.chat_completion(
-      messages=[{"role": "user", "content": f"{instructions}\n{prompt}"}],
-      max_tokens=500,
-      stream=True,
-    ):
-        response += message.choices[0].delta.content  # Append each message to the response
-    return response  # Return the accumulated response after the loop
+    print("Sending request to PHI 3 API...")
+    response = ""  
+    try:
+        for message in client.chat_completion(
+          messages=[{"role": "user", "content": f"{instructions}\n{prompt}"}],
+          max_tokens=500,
+          stream=True,
+        ):
+            response += message.choices[0].delta.content  
+    except Exception as e:
+        print("Error in PHI 3 API:", e)
+        return "PHI 3 API Error: " + str(e)
+    
+    return response
 
 def transcribe_and_query(audio):
-    # Transcribe the audio file
-    transcription = whisper(audio)["text"]
-    transcription = "Prompt : " + transcription
-    # Query Microsoft PHI 3 with the transcribed text
-    phi_response = query_phi(transcription)
+    try:
+        # Load the audio file as waveform
+        audio_data, sr = librosa.load(audio, sr=16000)
 
-    return transcription, phi_response
+        # Transcribe using Whisper
+        transcription = whisper(audio_data)["text"]
+        transcription = "Prompt : " + transcription
+
+        # Query Microsoft PHI 3 with the transcribed text
+        phi_response = query_phi(transcription)
+
+        return transcription, phi_response
+
+    except Exception as e:
+        return f"Error processing audio: {str(e)}", "No response from PHI 3"
 
 # Create Gradio interface
 iface = gr.Interface(
     fn=transcribe_and_query,
     inputs=gr.Audio(type="filepath"),
     outputs=["text", "text"],
-    title="Scam Call detector with BEEP",
-    description="Upload your recorded call to see if it is a scam or not. /n Stay Safe, Stay Secure."
+    title="Scam Call Detector with BEEP",
+    description="Upload your recorded call to see if it is a scam or not.\n Stay Safe, Stay Secure."
 )
 
 # Launch the interface
-iface.launch(share=True)  # share=True is optional, it provides a public link
+iface.launch(share=True)