Add progress bars

app.py

@@ -224,17 +224,19 @@ def upload_file_handler(files):
         return files
     return []
 
-async def generate_plan(history, file_cache):
+async def generate_plan(history, file_cache, progress=gr.Progress()):
     """Generate a plan using the planning prompt and Gemini API"""
     
     # Build conversation history
+    progress(0, desc="Starting")
+
     conversation_history = ""
     if history:
         for user_msg, ai_msg in history:
             conversation_history += f"User: {user_msg}\n"
             if ai_msg:
                 conversation_history += f"Assistant: {ai_msg}\n"
-    
+    progress(0.05, desc="Getting HF MCP tools")
     try:
         mcp_tool_func = modal.Function.from_name("HuggingFace-MCP","connect_and_get_tools")
         hf_query_gen_tool_details = mcp_tool_func.remote()
@@ -247,12 +249,15 @@ async def generate_plan(history, file_cache):
         Tool_Details=hf_query_gen_tool_details
     ) + "\n\n" + conversation_history
     # Get plan from Gemini
+    progress(0.15, desc="Strategizing which tools to call")
+
     plan = generate_with_gemini(formatted_prompt, "Planning with gemini")
-    
     # Parse the plan
     parsed_plan = parse_json_codefences(plan)
     print(parsed_plan)
     # Call tool to get tool calls
+    progress(0.50, desc="calling HF platform tools and getting data")
+
     try:
         mcp_call_tool_func = modal.Function.from_name(app_name="HuggingFace-MCP",name="call_tool")
         tool_calls = []
@@ -262,6 +267,8 @@ async def generate_plan(history, file_cache):
         print(str(e))
         tool_calls = []
     print(tool_calls)
+    progress(0.75, desc="Generating Plan context from tool call info")
+
     if tool_calls!=[]:
         formatted_context_prompt = hf_context_gen_prompt.format(
             Conversation=conversation_history,
@@ -277,12 +284,14 @@ async def generate_plan(history, file_cache):
             Results="Couldn't generate the tool calls results but use your knowledge about huggingface platform(models, datasets, spaces, training libraries, transfomers library etc.) as backup to generate the plan"
         )
         context = generate_with_gemini(formatted_context_prompt, "Generating context for plan")
+    progress(1, desc="Complete Plan generated")
 
     return context
 
-def generate_code_with_devstral(plan_text, history, file_cache):
+def generate_code_with_devstral(plan_text, history, file_cache, progress=gr.Progress()):
     """Generate code using the deployed Devstral model via Modal"""
-    
+    progress(0, desc="Starting Codegen")
+
     if not MODAL_AVAILABLE:
         return "❌ Modal not available. Please install Modal to use code generation."
     
@@ -332,6 +341,7 @@ def generate_code_with_devstral(plan_text, history, file_cache):
     api_key = os.getenv("DEVSTRAL_API_KEY")
     print(f"🚀 Generating code using Devstral...")
     print(f"📡 Connecting to: {base_url}")
+    progress(0.1, desc="Calling Devstral VLLM API server deployed on Modal")
 
     try:
         devstral_inference_func = modal.Function.from_name("devstral-inference-client", "run_devstral_inference")
@@ -343,16 +353,28 @@ def generate_code_with_devstral(plan_text, history, file_cache):
             mode="single"
         )
         if result and "response" in result:
+            progress(1, desc="Code has been generated")
+
             code_output = result["response"]
             return f"🚀 **Generated Code:**\n\n{code_output}"
         else:
+            progress(1, desc="Error")
+
             return "❌ **Error:** No response received from Devstral model."
     except Exception as e:
+        progress(1, desc="Error")
+
         return f"❌ **Error:** {str(e)}"
-def execute_code(code_output):
+def execute_code(code_output, progress=gr.Progress()):
+    progress(0, desc="Starting Code Execution")
+
     try:
+        progress(0.05, desc="Parsing Python codefence")
+
         code = parse_python_codefences(code_output)
         print(code)
+        progress(0.1, desc="Running code in sandbox")
+
         result = code_eval(code)
         if isinstance(result, dict):
             result_str = json.dumps(result, indent=4)
@@ -360,8 +382,13 @@ def execute_code(code_output):
             result_str = '\n'.join(str(x) for x in result)
         else:
             result_str = str(result)
+
+        progress(1, desc="Code Execution Complete")
+
         return result_str
     except Exception as e:
+        progress(1, desc="Error")
+
         return f"❌ **Error:** {str(e)}"
         
 # Custom CSS for a sleek design