Update app.py

app.py

@@ -1,64 +1,97 @@
 import gradio as gr
 from huggingface_hub import InferenceClient
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
+# Function to create InferenceClient dynamically based on model selection
+def get_client(model_name):
+    return InferenceClient(model_name)
 
 def respond(
     message,
     history: list[tuple[str, str]],
-    system_message,
     max_tokens,
     temperature,
     top_p,
+    model_name,  # Added model_name to the function arguments
 ):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
+    # Statically defined system message
+    system_message = "You are a friendly Chatbot."
+    
+    # Create client for the selected model
+    client = get_client(model_name)
+    
+    # Check if the model is one of the problematic models
+    if model_name in ["indonlp/cendol-mt5-small-inst", "indonlp/cendol-mt5-small-chat"]:
+        # For these models, we simply concatenate the conversation into a single string
+        history_str = ""
+        for user_msg, assistant_msg in history:
+            if user_msg:
+                history_str += f"{user_msg}\n"
+            if assistant_msg:
+                history_str += f"{assistant_msg}\n"
+        
+        # Add the latest user message
+        history_str += f"{message}\n"
+        
+        # Pass the entire conversation history as a plain text prompt
+        response = client.text_generation(
+            history_str,  # Single string as input
+            max_new_tokens=max_tokens,
+            temperature=temperature,
+            top_p=top_p
+        )
+        
+        # Since response is a string, return it directly
+        full_response = response
+    else:
+        # For other models, we use a structured format with roles
+        messages = [{"role": "system", "content": system_message}]
+        for val in history:
+            if val[0]:
+                messages.append({"role": "user", "content": val[0]})
+            if val[1]:
+                messages.append({"role": "assistant", "content": val[1]})
+        
+        # Add the latest user message
+        messages.append({"role": "user", "content": message})
+        
+        # Make the request
+        response = client.chat_completion(
+            messages,
+            max_tokens=max_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            stream=False
+        )
+        
+        # Extract the full response for chat models
+        full_response = response.choices[0].message["content"]
+    
+    return full_response
 
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
 
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+# Gradio ChatInterface setup with static system message and no Textbox for system message
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
         gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
         gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
         gr.Slider(
             minimum=0.1,
             maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
+            value=0.95, step=0.05, label="Top-p (nucleus sampling)"
+        ),
+        # Dropdown to select model
+        gr.Dropdown(
+            choices=[
+                "meta-llama/Meta-Llama-3-8B-Instruct",
+                "mistralai/Mistral-7B-Instruct-v0.3",
+                "HuggingFaceH4/zephyr-7b-beta"
+            ],
+            value="meta-llama/Meta-Llama-3-8B-Instruct",
+            label="Choose Model"
         ),
     ],
 )
 
-
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch()