Create app.py

app.py

@@ -0,0 +1,74 @@
+from fastapi import FastAPI, File, UploadFile, HTTPException
+import torch
+from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
+import requests
+import json
+import tempfile
+import os
+
+app = FastAPI()
+
+# Set up Whisper model
+device = "cuda:0" if torch.cuda.is_available() else "cpu"
+torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
+
+model_id = "openai/whisper-large-v3-turbo"
+
+model = AutoModelForSpeechSeq2Seq.from_pretrained(
+    model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
+)
+model.to(device)
+
+processor = AutoProcessor.from_pretrained(model_id)
+
+pipe = pipeline(
+    "automatic-speech-recognition",
+    model=model,
+    tokenizer=processor.tokenizer,
+    feature_extractor=processor.feature_extractor,
+    torch_dtype=torch_dtype,
+    device=device,
+)
+
+OPENROUTER_API_KEY = os.getenv("OPENROUTER_API_KEY", "")
+OPENROUTER_URL = "https://openrouter.ai/api/v1/chat/completions"
+
+@app.post("/transcribe-analyze/")
+async def transcribe_analyze(file: UploadFile = File(...)):
+    try:
+        # Save the uploaded file temporarily
+        with tempfile.NamedTemporaryFile(delete=False, suffix=".mp3") as temp_audio:
+            temp_audio.write(await file.read())
+            temp_audio_path = temp_audio.name
+
+        # Transcribe audio
+        transcription_result = pipe(temp_audio_path, return_timestamps=True)
+        transcription = transcription_result["text"]
+
+        # Send transcription to AI for classification
+        response = requests.post(
+            url=OPENROUTER_URL,
+            headers={
+                "Authorization": f"Bearer {OPENROUTER_API_KEY}",
+                "Content-Type": "application/json"
+            },
+            data=json.dumps({
+                "model": "meta-llama/llama-3.1-70b-instruct:free",
+                "messages": [
+                    {
+                        "role": "user",
+                        "content": f"You are an AI Assistant that is given the transcript between a call agent and a lead, and you must classify if the lead happily agreed to the booking. The response should have 4 parts: 1. Appointment Booked: Yes/No, 2. Short reason for your answer, 3. Short summary of the call, 4. Lead's overall emotion. \n Here is the transcription: {transcription}",
+                    }
+                ]
+            })
+        )
+
+        ai_response = response.json().get("choices", [{}])[0].get("message", {}).get("content", "No response from AI.")
+
+        # Remove temporary file
+        os.remove(temp_audio_path)
+
+        return {"transcription": transcription, "ai_response": ai_response}
+
+    except Exception as e:
+        return HTTPException(status_code=500, detail=str(e))