Update app.py

app.py

@@ -1,61 +1,61 @@
-import os
-from dotenv import load_dotenv
-import gradio as gr
-import openai
-from prompts import SYSTEM_PROMPT
-
-# 📥 Load environment variables
-load_dotenv()
-
-client = openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
-
-# 🔁 Transcribe audio input using OpenAI Whisper API
-def transcribe_audio(audio_file):
-    with open(audio_file, "rb") as f:
-        transcript = client.audio.transcriptions.create(
-            model="whisper-1",
-            file=f
-        )
-    return transcript.text
-
-# 💬 Call OpenAI chat with the transcribed or text input
-def call_openai(event_details):
-    prompt = SYSTEM_PROMPT.format(event_details)
-    response = client.chat.completions.create(
-        model="gpt-4o",
-        messages=[{"role": "user", "content": prompt}],
-        temperature=0.5,
-        max_tokens=1024,
-    )
-    return response.choices[0].message.content
-
-# 🔄 New function to handle audio input and route to existing pipeline
-def case_study_from_audio(audio):
-    text = transcribe_audio(audio)
-    return case_study_generator(text)
-
-# 🎛️ Original function — unchanged
-def case_study_generator(event_details):
-    gpt_response = call_openai(event_details)
-    return gpt_response
-
-# 🚀 Gradio interface with two modes: Text or Audio
-with gr.Blocks() as demo:
-    gr.Markdown("## 📄 iBoothMe Case Study Generator\nWrite a description or record audio.")
-    with gr.Tab("Text Input"):
-        text_input = gr.Textbox(
-            label="Event Description",
-            lines=10,
-            placeholder="Paste informal event description here...",
-        )
-        text_button = gr.Button("Generate from Text")
-        text_output = gr.Textbox(label="Generated Case Study", lines=15)
-        text_button.click(fn=case_study_generator, inputs=text_input, outputs=text_output)
-
-    with gr.Tab("Audio Input"):
-        audio_input = gr.Audio(type="filepath", label="Upload or Record your event description")
-        audio_button = gr.Button("Generate from Audio")
-        audio_output = gr.Textbox(label="Generated Case Study", lines=15)
-        audio_button.click(fn=case_study_from_audio, inputs=audio_input, outputs=audio_output)
-
-demo.launch()
+import os
+from dotenv import load_dotenv
+import gradio as gr
+import openai
+from prompts import SYSTEM_PROMPT
+
+# 📥 Load environment variables
+load_dotenv()
+
+client = openai.OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
+
+# 🔁 Transcribe audio input using OpenAI Whisper API
+def transcribe_audio(audio_file: str) -> str:
+    with open(audio_file, "rb") as f:
+        transcript = client.audio.transcriptions.create(
+            model="whisper-1",
+            file=f
+        )
+    return transcript.text
+
+# 💬 Call OpenAI chat with the transcribed or text input
+def call_openai(event_details: str) -> str:
+    prompt = SYSTEM_PROMPT.format(event_details)
+    response = client.chat.completions.create(
+        model="gpt-4o",
+        messages=[{"role": "user", "content": prompt}],
+        temperature=0.5,
+        max_tokens=1024,
+    )
+    return response.choices[0].message.content
+
+# 🔄 New function to handle audio input and route to existing pipeline
+def case_study_from_audio(audio: str) -> str:
+    text = transcribe_audio(audio)
+    return case_study_generator(text)
+
+# 🎛️ Original function — unchanged but now typed
+def case_study_generator(event_details: str) -> str:
+    return call_openai(event_details)
+
+# 🚀 Gradio interface with two modes: Text or Audio
+with gr.Blocks() as demo:
+    gr.Markdown("## 📄 iBoothMe Case Study Generator\nWrite a description or record audio.")
+    
+    with gr.Tab("Text Input"):
+        text_input = gr.Textbox(
+            label="Event Description",
+            lines=10,
+            placeholder="Paste informal event description here...",
+        )
+        text_button = gr.Button("Generate from Text")
+        text_output = gr.Textbox(label="Generated Case Study", lines=15)
+        text_button.click(fn=case_study_generator, inputs=text_input, outputs=text_output)
+
+    with gr.Tab("Audio Input"):
+        audio_input = gr.Audio(type="filepath", label="Upload or Record your event description")
+        audio_button = gr.Button("Generate from Audio")
+        audio_output = gr.Textbox(label="Generated Case Study", lines=15)
+        audio_button.click(fn=case_study_from_audio, inputs=audio_input, outputs=audio_output)
+
+demo.launch()