Update app.py

app.py

@@ -1,43 +1,28 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 
+client = InferenceClient("google/gemma-1.1-2b-it")
 
-def client_fn(model):
-    if "Nous" in model:
-        return InferenceClient("NousResearch/Nous-Hermes-2-Mixtral-8x7B-DPO")
-    elif "Star" in model:
-        return InferenceClient("HuggingFaceH4/starchat2-15b-v0.1")
-    elif "Mistral" in model:
-        return InferenceClient("mistralai/Mistral-7B-Instruct-v0.3")
-    elif "Phi" in model:
-        return InferenceClient("microsoft/Phi-3-mini-4k-instruct")
-    else: 
-        return InferenceClient("google/gemma-1.1-2b-it")
-
-system_instructions1 = "[SYSTEM] Your task is to Answer the question. Keep conversation very short, clear and concise. The expectation is that you will avoid introductions and start answering the query directly, Only answer the question asked by user, Do not say unnecessary things.[QUESTION]"
-
-def models(text, model="Mixtral 8x7B"): 
-    
-    client = client_fn(model)
-    
-    generate_kwargs = dict(
-        max_new_tokens=100,
-        do_sample=True,
-    )
+system_instructions = "[SYSTEM] Your task is to Answer the question. Keep conversation very short, clear and concise. The expectation is that you will avoid introductions and start answering the query directly, Only answer the question asked by user, Do not say unnecessary things.[QUESTION]"
+
+def models(message): 
     
-    formatted_prompt = system_instructions1 + text + "[ANSWER]"
-    stream = client.text_generation(formatted_prompt, **generate_kwargs, stream=True, details=True, return_full_text=False)
-    output = ""
-    for response in stream:        
-        output+=response.token.text
-        if output.endswith("<|assistant|>"):
-            output = output[:-13]
-        elif output.endswith("</s>"):
-            output = output[:-4]
-    return output
+    messages.append({"role": "user", "content": f"[SYSTEM] You are REAL TIME QnA AI who answer question asked by uer in short and concise [USER] {message}"})
+
+    response = ""
+
+    for message in client.chat_completion(
+        messages,
+        max_tokens=200,
+        stream=True
+    ):
+        token = message.choices[0].delta.content
+
+        response += token
+        yield response
 
 description="# Chat GO"
 
-demo = gr.Interface(description=description,fn=models, inputs=["text", gr.Dropdown([ 'Mixtral 8x7B','Nous Hermes Mixtral 8x7B DPO','StarChat2 15b','Mistral 7B v0.3','Phi 3 mini', 'FASTEST' ], value="FASTEST", label="Select Model") ], outputs="text", live=True, batch=True, max_batch_size=10000)
+demo = gr.Interface(description=description,fn=models, inputs=["text"], outputs="text", live=True, batch=True, max_batch_size=10000)
 demo.queue(max_size=300000)
 demo.launch()