Update app.py

app.py

@@ -2,7 +2,7 @@ import gradio as gr
 from huggingface_hub import InferenceClient
 
 # Initialize clients
-text_client = InferenceClient("mistralai/Mistral-Nemo-Instruct-2407")
+text_client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 image_client = InferenceClient("SG161222/RealVisXL_V3.0")
 
 def check_custom_responses(message: str) -> str:
@@ -187,6 +187,16 @@ def respond(
         yield custom_response
         return
 
+    if is_image_request(message):
+        try:
+            image = generate_image(message)
+            if image:
+                return f"Here's your generated image based on: {message}"
+            else:
+                return "Sorry, I couldn't generate the image. Please try again."
+        except Exception as e:
+            return f"An error occurred while generating the image: {str(e)}"
+
     # Prepare conversation history
     messages = [{"role": "system", "content": system_message}]
     for val in history:
@@ -197,61 +207,18 @@ def respond(
     
     messages.append({"role": "user", "content": message})
 
-    # Check if the user wants a fast or critical response
-    if "critical" in message.lower():
-        # Critical thinker logic (like the 'nemo' function)
-        budget = 3  # Starting budget for steps
-        step_response = ""
-        message_critical = f"""[INST] [SYSTEM] You are a helpful assistant in normal conversation.
-When given a problem to solve, you are an expert problem-solving assistant.
-Your task is to provide a detailed, step-by-step solution to a given question.
-Follow these instructions carefully:
-1. Read the given question carefully and reset counter between <count> and </count> to {budget} (maximum 3 steps).
-2. Think critically like a human researcher or scientist. Break down the problem using first principles to conceptually understand and answer the question.
-3. Generate a detailed, logical step-by-step solution.
-4. Enclose each step of your solution within <step> and </step> tags.
-5. You are allowed to use at most {budget} steps (starting budget), keep track of it by counting down within tags <count> </count>, STOP GENERATING MORE STEPS when hitting 0, you don't have to use all of them.
-6. Do a self-reflection when you are unsure about how to proceed, based on the self-reflection and reward, decide whether you need to return to the previous steps.
-7. After completing the solution steps, reorganize and synthesize the steps into the final answer within <answer> and </answer> tags.
-8. Provide a critical, honest, and subjective self-evaluation of your reasoning process within <reflection> and </reflection> tags.
-9. Assign a quality score to your solution as a float between 0.0 (lowest quality) and 1.0 (highest quality), enclosed in <reward> and </reward> tags.
-Example format:
-<count> [starting budget] </count>
-<step> [Content of step 1] </step>
-<count> [remaining budget] </count>
-<step> [Content of step 2] </step>
-<reflection> [Evaluation of the steps so far] </reflection>
-<reward> [Float between 0.0 and 1.0] </reward>
-<count> [remaining budget] </count>
-<step> [Content of step 3 or Content of some previous step] </step>
-<count> [remaining budget] </count>
-...
-<step> [Content of final step] </step>
-<count> [remaining budget] </count>
-<answer> [Final Answer] </answer> (must give final answer in this format)
-<reflection> [Evaluation of the solution] </reflection>
-<reward> [Float between 0.0 and 1.0] </reward> [/INST] [INST] [QUERY] {message} [/INST] [ASSISTANT] """
-        
-        # Send critical thinking message to client and stream response
-        stream = text_client.text_generation(message_critical, max_new_tokens=4096, stream=True, details=True, return_full_text=False)
-        critical_response = ""
-        for response in stream:
-            critical_response += response.token.text
-        yield critical_response
-        
-    else:
-        # Standard fast response logic (like 'models')
-        response = ""
-        for message in text_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
+    # Get response from model
+    response = ""
+    for message in text_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
 
     yield response
 
@@ -293,6 +260,7 @@ Focus on accuracy, respect for sensitive topics, transparency, and professionali
 Version: Xylaria-1.2.9
 """
 
+
 # Gradio chat interface
 demo = gr.ChatInterface(
     respond,
@@ -325,4 +293,4 @@ demo = gr.ChatInterface(
     ],
     css=custom_css
 )
-demo.launch()
+demo.launch()