Update app.py

app.py

@@ -1,101 +1,35 @@
-import gradio as gr 
-
-import requests 
-import json
-import numpy as np
-# from sklearn.metrics.pairwise import cosine_similarity
-import pickle
+import gradio as gr
 import os
-from tqdm import tqdm
-import ast
-
-dd= os.getenv('funns')
-
-exec(dd)
-
-
+from groq import Groq
 
-######################
-# App
-#######################
+# Initialize the Groq client
+client = Groq(api_key=os.environ["keko"])
 
-
-# def process_input(case):
-#     if case.strip():
-#         # Generate interpretations
-#         all_interpretations = generate_interpretations(case)
-#         yield all_interpretations, gr.update()  # Use gr.update() instead of gr.Textbox.update()
-        
-#         # Prepare input for final answer
-#         # explanation_input = f'Case: \n\n{case}\n\nInterpretation: {all_interpretations}\n\nReferences: {reff}'
-#         explanation_input = f'Case: \n\n{case}\n\nInterpretation: {all_interpretations}'
-#         explanation_sys_prompt2 = f'{explanation_sys_prompt}  \n\nReferences: {reff}'
-
-        
-        
-#         # Generate final answer
-#         f_answer = generate_text(explanation_input, explanation_sys_prompt2, temperature=0.2, model="gpt-4o-mini", max_tokens=3500)
-        
-#         yield all_interpretations, f_answer
-#     else:
-#         yield "Please enter some text.", ""
-
-# with gr.Blocks(title="GI Assist") as demo:
-#     gr.Markdown("# GI Assist")
-#     gr.Markdown("Enter your case and click submit.")
-    
-#     with gr.Row():
-#         input_text = gr.Textbox(lines=5, placeholder="Enter your case here...")
-    
-#     with gr.Row():
-#         submit_btn = gr.Button("Submit")
+def transcribe_audio(audio_file):
+    if audio_file is None:
+        return "No audio file provided."
     
-#     with gr.Row():
-#         interpretations_output = gr.Textbox(label="Interpretations:", lines=6)
-#         medical_opinion_output = gr.Textbox(label="Medical opinion:", lines=10)
+    # Create a transcription of the audio file
+    with open(audio_file, "rb") as file:
+        transcription = client.audio.transcriptions.create(
+            file=(audio_file, file.read()),
+            model="distil-whisper-large-v3-en",
+            prompt="Specify context or spelling",
+            response_format="json",
+            language="en",
+            temperature=0.0
+        )
     
-#     submit_btn.click(
-#         process_input,
-#         inputs=[input_text],
-#         outputs=[interpretations_output, medical_opinion_output]
-#     )
-
-# demo.queue()
-# demo.launch()
-
-
-
-
-
-
-
-
-
-def process_input(case):
-    if case.strip():
-        all_interpretations = generate_interpretations(case)
-        
-        
-        explanation_input = f'Case: \n\n{case}\n\nInterpretation: {all_interpretations}'
-        explanation_sys_prompt2 = f'{explanation_sys_prompt}  \n\nReferences: {reff}'        
-        
-        f_answer = generate_text(explanation_input, explanation_sys_prompt2, temperature=0.2, model="gpt-4o-mini", max_tokens=3500)
-        
-        return all_interpretations, f_answer
-    else:
-        return "Please enter some text.", ""
+    return transcription.text
 
 # Create the Gradio interface
 iface = gr.Interface(
-    fn=process_input,
-    inputs=gr.Textbox(lines=5, placeholder="Enter your case here..."),
-    outputs=[
-        gr.Textbox(label="Interpretations:", lines=6),
-        gr.Textbox(label="Medical opinion:", lines=10)
-    ],
-    title="GI Assist",
-    description="Enter your case and click submit to generate a response using ChatGPT."
+    fn=transcribe_audio,
+    inputs=gr.Audio(source="microphone", type="filepath"),
+    outputs="text",
+    title="Audio Transcription with Groq",
+    description="Record audio and get its transcription using Groq's speech-to-text API."
 )
 
-# Launch the app
-iface.launch()
+# Launch the interface
+iface.launch()