Update app.py

app.py

@@ -66,6 +66,7 @@ RAG_CHAIN_PROMPT = PromptTemplate(input_variables = ["context", "question"],
 #Plattform Keys aus den Secrets holen zu diesem Space
 HUGGINGFACEHUB_API_TOKEN = os.getenv("HF_ACCESS_READ")
 OAI_API_KEY=os.getenv("OPENAI_API_KEY")
+HEADERS = {"Authorization": f"Bearer {HUGGINGFACEHUB_API_TOKEN}"}
 
 
 #Pfad, wo Docs/Bilder/Filme abgelegt werden können - lokal, also hier im HF Space (sonst auf eigenem Rechner)
@@ -111,6 +112,8 @@ repo_id = "HuggingFaceH4/zephyr-7b-alpha"   #das Modell ist echt gut!!! Vom MIT
 #HuggingFace Model name--------------------------------
 MODEL_NAME_HF  = "mistralai/Mixtral-8x7B-Instruct-v0.1"
 MODEL_NAME_OAI_ZEICHNEN = "dall-e-3"
+#Alternativ zeichnen: Stabe Diffusion from HF:
+API_URL = "https://api-inference.huggingface.co/models/stabilityai/stable-diffusion-2-1"
 
 ################################################
 #HF Hub Zugriff ermöglichen
@@ -386,8 +389,9 @@ def invoke (prompt, file, history, rag_option, model_option, openai_api_key, k=3
         if (model_option == "OpenAI"):
             #Anfrage an OpenAI ----------------------------
             if (prompt.find('Bild zeichnen') != -1):
-                print("OpenAI zeichnen.......................")
-                llm = ChatOpenAI(model_name = MODEL_NAME_OAI_ZEICHNEN,  openai_api_key = openai_api_key, temperature=temperature)#, top_p = top_p)    
+                #print("OpenAI zeichnen.......................")
+                #llm = ChatOpenAI(model_name = MODEL_NAME_OAI_ZEICHNEN,  openai_api_key = openai_api_key, temperature=temperature)#, top_p = top_p)  
+                data = {"inputs": prompt}
             else: 
                 print("OpenAI normal.......................")
                 llm = ChatOpenAI(model_name = MODEL_NAME,  openai_api_key = openai_api_key, temperature=temperature)#, top_p = top_p)
@@ -416,8 +420,12 @@ def invoke (prompt, file, history, rag_option, model_option, openai_api_key, k=3
             result = rag_chain(llm, history_text_und_prompt, db)
         else:
             print("LLM aufrufen ohne RAG: ...........")
-            result = llm_chain(llm, history_text_und_prompt)
-            print(result)
+            if (prompt.find('Bild zeichnen') != -1):
+                response = requests.post(API_URL, headers=HEADERS, json=data)
+                result = response.content
+            else:
+                result = llm_chain(llm, history_text_und_prompt)
+            
 
     except Exception as e:
         raise gr.Error(e)