Text Question feature.

app.py

@@ -22,7 +22,7 @@ import torch
 
 load_dotenv()
 
-title = 'Detect and Segment Brain Tumors 🧠'
+title = 'Dr Brain Tumors 🧠'
 description = '''
 '''
 
@@ -92,7 +92,7 @@ openai.api_key = os.environ.get("OPENAI_KEY")
 
 
 def make_llm_call(prompt,
-                  context="You are a text generation model DR-Brain Developed by team brute force team consist of HARSHA VARDHAN V , SAWIN KUMAR Y , CHARAN TEJA P, KISHORE S. Your specialized in medical stuff"):
+                  context="You are a text generation model DR-Brain Developed by team brute force a team 4 AI engineers from RMKCET college they are HARSHA VARDHAN V , SAWIN KUMAR Y , CHARAN TEJA P, KISHORE S. Your specialized in medical stuff"):
     messages = [{"role": "user", "content": prompt}]
     if context:
         messages.insert(0, {"role": "system", "content": context})
@@ -101,9 +101,11 @@ def make_llm_call(prompt,
     return response_message
 
 
-def detector(tumor_file, slice_number, channel, language, audio_question):
+def detector(tumor_file, slice_number, channel, language, audio_question, text_question):
     llm_answer = "Hi I'm Dr brain please enter a question to answer"
-    if audio_question:
+    if text_question:
+        llm_answer = make_llm_call(text_question)
+    elif audio_question:
         sampling_rate, waveform = audio_question
         forced_decoder_ids = processor_whisper.get_decoder_prompt_ids(language=language, task="transcribe")
         waveform = process_audio(sampling_rate, waveform)
@@ -137,7 +139,8 @@ interface = gr.Interface(fn=detector, inputs=[gr.File(label="Tumor File"),
                                               gr.Slider(0, 200, 50, step=1, label="Slice Number"),
                                               gr.Radio((0, 1, 2), label="Channel"),
                                               gr.Radio(("english", "japanese", "german", "spanish"), label="Language"),
-                                              gr.Audio(info="Ask our medical specialist", source="microphone"), ],
+                                              gr.Audio(source="microphone"),
+                                              gr.Textbox(label='Text Question')],
                          outputs=[gr.Image(label='channel', shape=(1, 1)),
                                   gr.Image(label='Segmented Tumor', shape=(1, 1)),
                                   gr.Textbox(label="Dr brain response")], title=title,