Update app.py

app.py

@@ -12,7 +12,6 @@ import pyttsx3
 api_key = os.getenv('groq')
 # Initialisiere ChromaDB
 client_chroma = chromadb.Client()
-#client_croma = chromadb.PersistentClient(path="/")
 collection_name = "pdf_collection"
 collection = client_chroma.get_or_create_collection(name=collection_name)
 
@@ -78,21 +77,9 @@ def ask_llm(llm_prompt_input):
     
     # Füge die formatierten Ergebnisse zum Prompt hinzu
     enriched_prompt = f"{llm_prompt_input}\n\n### Verwandte Informationen:\n{''.join(formatted_results)}"
-    #print(enriched_prompt)
     result = update(enriched_prompt)
     result=gr.Markdown(result)
     return result
-    # Führe die Abfrage des LLM durch
-    result = client.predict(
-        query=enriched_prompt,
-        history=[],
-        system="You are Qwen, created by Alibaba Cloud. You are a helpful assistant.",
-        api_name="/model_chat"
-    )
-    result = result[1]
-    result = result[0][1]
-    result=gr.Markdown(result)   
-    return result
  
 def process_pdf(file):
     # Read the PDF content
@@ -182,25 +169,15 @@ with gr.Blocks() as speech:
         sr_outputs = gr.Textbox(label="Antwort")
     with gr.Row():
         sr_inputs = gr.Microphone(type="filepath")
-        
-        #sr_inputs = gr.Microphone(type="filepath")
-    #with gr.Row():
-        #audio_button = gr.Button("audio")
-    #audio_button.click(text_to_speech("guten tag wie geht es dir"))    
+   
     sr_inputs.change(transcribe_audio, inputs=sr_inputs, outputs=sr_outputs)
-    
-    #with gr.Row():
-        #submit_button = gr.Button("rec")
-    
-    #submit_button.click(transcribe_audio, inputs=sr_inputs, outputs=sr_outputs)
- 
 
 # Erstelle die Gradio-Schnittstelle
 with gr.Blocks() as demo:
     gr.TabbedInterface(
         [chat, upload, suche]
+        ["Chat", "Upload", "Suche"]
     )
 
-
 # Starte die Gradio-Anwendung
 demo.launch()