Create app.py

app.py

@@ -0,0 +1,55 @@
+import torch
+import torchaudio
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+import gradio as gr
+
+# 1️⃣ Load Whisper model for Speech-to-Text
+asr_pipe = pipeline("automatic-speech-recognition", model="openai/whisper-small")
+
+# 2️⃣ Load Qwen-style LLM for text response
+model_name = "Qwen/Qwen1.5-0.5B-Chat"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto",torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32)
+
+# Bot reply generator
+def generate_response(user_text):
+    messages = [
+        {"role": "system", "content": "You are a helpful assistant."},
+        {"role": "user", "content": user_text}
+    ]
+    
+    # Use chat template formatting
+    input_ids = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)
+    
+    with torch.no_grad():
+        output = model.generate(input_ids, max_new_tokens=150, pad_token_id=tokenizer.eos_token_id)
+    
+    decoded = tokenizer.decode(output[0], skip_special_tokens=True)
+    
+    # Return only the assistant's message
+    response = decoded.split("assistant")[-1].strip().replace(":", "").strip()
+    
+    return response
+
+
+# Complete pipeline: Audio → Text → Response
+def audio_to_bot_response(audio_path):
+    print("[INFO] Transcribing audio...")
+    result = asr_pipe(audio_path)
+    user_text = result['text']
+    
+    
+    print(f"[INFO] Transcribed: {user_text}")
+    response = generate_response(user_text)
+    
+    return f"👤 You said: {user_text}\n🤖 Bot: {response}"
+
+interface = gr.Interface(
+    fn=audio_to_bot_response,
+    inputs=gr.Audio(sources=["microphone"], type="filepath"),
+    outputs="text",
+    title="🎙 Voice to AI Bot Response",
+    description="Speak into the mic. The AI will transcribe and respond."
+)
+
+interface.launch()