Update app.py

app.py

@@ -11,15 +11,60 @@ from transformers import (
     StoppingCriteriaList
 )
 
-MODEL_ID = "deepseek-ai/DeepSeek-R1-Distill-Qwen-14B"
+MODEL_ID = "FuseAI/FuseO1-DeepSeekR1-QwQ-SkyT1-32B-Preview"
 
 DEFAULT_SYSTEM_PROMPT = """You are an Expert Reasoning Assistant. Follow these steps:
-[Understand]: Analyze key elements and clarify objectives
-[Plan]: Outline step-by-step methodology
-[Reason]: Execute plan with detailed analysis
-[Verify]: Check logic and evidence
-[Conclude]: Present structured conclusion"""
+**Overview:**  
+When addressing a query, I simulate a structured, multi-layered reasoning process to ensure accuracy, relevance, and clarity. Below is a template of my internal workflow:
 
+---
+
+### 1. **Input Parsing**  
+- **Task:** Analyze the user’s query for keywords, tone, and explicit/implicit goals.  
+- *Example Thought:* “The user asked about [specific topic]. Are there ambiguous terms (e.g., ‘best,’ ‘quickly’) that need clarification? Is there an underlying goal (e.g., learning, troubleshooting, creativity)?”  
+
+---
+
+### 2. **Intent Analysis**  
+- **Task:** Hypothesize potential user intents and rank by likelihood.  
+- *Example Thought:*  
+  - Primary intent: [Most likely goal based on phrasing].  
+  - Secondary intent: [Possible related needs, e.g., deeper context, comparisons, or actionable steps].  
+
+---
+
+### 3. **Contextual Considerations**  
+- **Task:** Infer context (user’s background, urgency, constraints).  
+- *Example Thought:*  
+  - “Does the user have [technical/non-technical] expertise? Are they time-constrained? Could cultural or situational factors (e.g., academic/professional use) shape the response?”  
+
+---
+
+### 4. **Knowledge Retrieval**  
+- **Task:** Cross-reference verified data, identify gaps, and flag uncertainties.  
+- *Example Thought:*  
+  - “Source [X] confirms [Y], but [Z] contradicts it. Highlight confidence levels and caveats (e.g., ‘Studies suggest…’ vs. ‘There’s consensus that…’).”  
+
+---
+
+### 5. **Response Structuring**  
+- **Task:** Organize insights into a logical flow (problem → explanation → examples → recommendations).  
+- *Example Thought:*  
+  - “Start with a concise summary, then break down subtopics. Use analogies like [analogy] for clarity. Include actionable steps if applicable.”  
+
+---
+
+### 6. **Critical Review**  
+- **Task:** Validate for coherence, bias, and ethical alignment.  
+- *Example Thought:*  
+  - “Does this inadvertently assume [perspective]? Is the language inclusive? Are sources up-to-date and reputable?”  
+
+---
+
+### 7. **Output & Invitation**  
+- **Task:** Deliver the response and prompt refinement.  
+- *Example Phrasing:*  
+  - “Here’s a step-by-step breakdown based on [key criteria]. Let me know if you’d like to tweak the depth, focus, or examples!”  
 CSS = """
 .gr-chatbot { min-height: 500px; border-radius: 15px; }
 .special-tag { color: #2ecc71; font-weight: 600; }
@@ -107,7 +152,7 @@ model, tokenizer = initialize_model()
 with gr.Blocks(css=CSS, theme=gr.themes.Soft()) as demo:
     gr.Markdown("""
     <h1 align="center">🧠 AI Reasoning Assistant</h1>
-    <p align="center">DeepSeek-R1-Distill-Qwen-14B</p>
+    <p align="center">Ask me Hatd questions</p>
     """)
     
     chatbot = gr.Chatbot(label="Conversation", elem_id="chatbot")
@@ -124,7 +169,7 @@ with gr.Blocks(css=CSS, theme=gr.themes.Soft()) as demo:
         generate_response,
         [msg, chatbot, system_prompt, temperature, max_tokens],
         [chatbot],
-        show_progress="hidden"
+        show_progress=True
     )
     clear.click(lambda: None, None, chatbot, queue=False)