Isolate questions code in own module to ease debugging

src/interviewkit/cli.py

@@ -4,6 +4,7 @@
 from pathlib import Path
 from typing_extensions import Annotated
 
+from questions import generate_questions_from_transcript
 from slicer import audio_slicing
 from transcript import transcribe_from_paths
 
@@ -41,6 +42,25 @@ def slice(
     """Slices an audio file into smaller audio files."""
     audio_slicing(source, start, duration)
 
+@app.command()
+def generate_questions(source: Annotated[
+        Path,
+        typer.Argument(
+            exists=True,
+            file_okay=True,
+            dir_okay=False,
+            readable=True,
+            resolve_path=True,
+            help="Source transcript file",
+        ),
+    ],
+    target: Path
+    ):
+    """Generates questions from a transcript."""
+
+    questions = generate_questions_from_transcript(source.read_text())
+    target.write_text(questions)
+
 
 @app.command()
 def transcribe(

src/interviewkit/questions.py

@@ -0,0 +1,50 @@
+from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel
+from clarifai_grpc.grpc.api import resources_pb2, service_pb2, service_pb2_grpc
+from clarifai_grpc.grpc.api.status import status_code_pb2
+
+# # Securely get your credentials
+# TODO: Pass in arguments or use env vars
+CLARIFAI_PAT = ''
+# Specify the correct user_id/app_id pairings
+# Since you're making inferences outside your app's scope
+CLARIFAI_USER_ID = 'meta'
+CLARIFAI_APP_ID = 'Llama-2'
+# Change these to whatever model and text URL you want to use
+CLARIFAI_MODEL_ID = 'llama2-70b-chat'
+CLARIFAI_MODEL_VERSION_ID = 'acba9c1995f8462390d7cb77d482810b'
+
+
+def generate_questions_from_transcript(transcript: str):
+
+    channel = ClarifaiChannel.get_grpc_channel()
+    stub = service_pb2_grpc.V2Stub(channel)
+
+    metadata = (('authorization', 'Key ' + CLARIFAI_PAT),)
+    userDataObject = resources_pb2.UserAppIDSet(
+        user_id=CLARIFAI_USER_ID, app_id=CLARIFAI_APP_ID)
+
+    post_model_outputs_response = stub.PostModelOutputs(
+        service_pb2.PostModelOutputsRequest(
+            user_app_id=userDataObject,
+            model_id=CLARIFAI_MODEL_ID,
+            version_id=CLARIFAI_MODEL_VERSION_ID,
+            inputs=[
+                resources_pb2.Input(
+                    data=resources_pb2.Data(
+                        text=resources_pb2.Text(
+                            raw=transcript
+                        )
+                    )
+                )
+            ]
+        ),
+        metadata=metadata
+    )
+
+    if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
+        print(post_model_outputs_response.status)
+        status = post_model_outputs_response.status.description
+        raise Exception(f"Post model outputs failed, status: {status}")
+
+    output = post_model_outputs_response.outputs[0]
+    return output.data.text.raw

src/interviewkit/transcript.py

@@ -1,9 +1,6 @@
 from pathlib import Path
 from rich.console import Console
 import sys
-from clarifai_grpc.channel.clarifai_channel import ClarifaiChannel
-from clarifai_grpc.grpc.api import resources_pb2, service_pb2, service_pb2_grpc
-from clarifai_grpc.grpc.api.status import status_code_pb2
 
 try:
     import whisper
@@ -20,53 +17,6 @@ class Transcript(BaseModel):
     """The Transcript entity represents the transcript of an interview."""
     content: str
 
-# # Securely get your credentials
-# PAT = os.getenv('CLARIFAI_PAT')
-# USER_ID = os.getenv('CLARIFAI_USER_ID')
-# APP_ID = os.getenv('CLARIFAI_APP_ID')
-# MODEL_ID = os.getenv('CLARIFAI_MODEL_ID')
-# MODEL_VERSION_ID = os.getenv('CLARIFAI_MODEL_VERSION_ID')
-PAT = ''
-# Specify the correct user_id/app_id pairings
-# Since you're making inferences outside your app's scope
-USER_ID = 'meta'
-APP_ID = 'Llama-2'
-# Change these to whatever model and text URL you want to use
-MODEL_ID = 'llama2-70b-chat'
-MODEL_VERSION_ID = 'acba9c1995f8462390d7cb77d482810b'
-
-def generate_questions(transcript_chunk):
-    channel = ClarifaiChannel.get_grpc_channel()
-    stub = service_pb2_grpc.V2Stub(channel)
-
-    metadata = (('authorization', 'Key ' + PAT),)
-    userDataObject = resources_pb2.UserAppIDSet(user_id=USER_ID, app_id=APP_ID)
-
-    post_model_outputs_response = stub.PostModelOutputs(
-        service_pb2.PostModelOutputsRequest(
-            user_app_id=userDataObject,
-            model_id=MODEL_ID,
-            version_id=MODEL_VERSION_ID,
-            inputs=[
-                resources_pb2.Input(
-                    data=resources_pb2.Data(
-                        text=resources_pb2.Text(
-                            raw=transcript_chunk
-                        )
-                    )
-                )
-            ]
-        ),
-        metadata=metadata
-    )
-
-    if post_model_outputs_response.status.code != status_code_pb2.SUCCESS:
-        print(post_model_outputs_response.status)
-        raise Exception(f"Post model outputs failed, status: {post_model_outputs_response.status.description}")
-
-    output = post_model_outputs_response.outputs[0]
-    return output.data.text.raw
-
 def transcribe_from_paths(source: Path, target: Path) -> None:
     console.print("Loading whisper base model...")
     model = whisper.load_model("base")
@@ -89,20 +39,6 @@ def transcribe_from_paths(source: Path, target: Path) -> None:
     console.print("Transcript saved to:")
     console.print(f"    [green bold]{target / source.name}.txt[/green bold]")
 
-    # Generate questions from the transcript
-    transcript_chunk = result['text']  # Assuming 'result' contains the transcribed text
-    # Debug: Print type and value of transcript_chunk
-    print(f"Type of transcript_chunk: {type(transcript_chunk)}")
-    print(f"Value of transcript_chunk: {transcript_chunk}")
-
-    # Ensure transcript_chunk is a string
-    if not isinstance(transcript_chunk, str):
-        print("Warning: transcript_chunk is not a string. Trying to convert...")
-        transcript_chunk = str(transcript_chunk)
-
-    questions = generate_questions(transcript_chunk)
-    console.print("Generated Questions:\n", questions)
-
 if __name__ == "__main__":
     source = Path(sys.argv[1])
     target = Path(sys.argv[2])