Update app.py

app.py

@@ -4,22 +4,24 @@ import instructor
 from openai import OpenAI
 import os
 import json
+import logging
 
 load_dotenv()
 
+# Configure logging
+logging.basicConfig(level=logging.INFO)
+
 # Patch the OpenAI client with Instructor
 client = instructor.from_openai(OpenAI(api_key=os.environ['OPENAI_API_KEY']))
 
 # Define prompt templates
 PRD_PROMPT_TEMPLATE = """
 ### Product Requirements Document (PRD) for AI Applications
-
 #### Introduction
 - **Objective/Goal**: 
   - Explain the purpose of the AI application and what it aims to achieve.
   - Provide a high-level overview of the release purpose.
   - User Proposal: {user_proposal}
-
 #### Features
 - **Feature 1**: 
   - **Description**: Detailed description of the feature.
@@ -32,20 +34,16 @@ PRD_PROMPT_TEMPLATE = """
   - **Goal**: What this feature aims to achieve.
   - **Use Case**: Example of how a user would utilize this feature.
   - **Additional Details**: Specifics such as out-of-scope items and prompt structure.
-
 *(Repeat for each feature)*
-
 #### UX Flow & Design Notes
 - **Overview**: General guidance required to ensure the release objectives are met.
 - **User Workflow**: Describe the overall user workflow.
 - **Design Considerations**: Any specific design notes relevant at this stage.
-
 #### System & Environment Requirements
 - **Supported Environments**: 
   - Browsers (e.g., Chrome, Firefox, Safari)
   - Operating Systems (e.g., Windows 10, macOS)
   - Memory and Processing Power requirements
-
 #### Assumptions, Constraints & Dependencies
 - **Assumptions**: 
   - List anything expected to be in place (e.g., all users will have Internet connectivity).
@@ -55,18 +53,15 @@ PRD_PROMPT_TEMPLATE = """
   
 - **Dependencies**: 
   - Conditions or items the product will rely on (e.g., relying on Google Maps for directions).
-
 #### Prompt Engineering Practices
 - **Ambiguity Mitigation**: Ensure clear, specific prompts to avoid ambiguous outputs. Evaluate prompts using examples and test consistency with different inputs.
 - **Versioning**: Track and version each prompt to monitor performance changes over time.
 - **Optimization**: Apply techniques like chain-of-thought prompting, majority voting, and breaking tasks into smaller prompts to improve output quality.
 - **Cost & Latency Management**: Balance detailed prompts with cost efficiency and manage latency by optimizing token usage.
-
 #### Task Composability
 - **Multiple Tasks Composition**: Outline how multiple tasks interact, including sequential and parallel executions, and control flows like if statements and loops.
 - **Agents and Tools**: Describe the use of agents to manage tasks and the integration of tools such as SQL executors and web browsers.
 - **Control Flows**: Detail the use of control flows in managing tasks, including sequential, parallel, if statements, and loops.
-
 #### Review & Approval Process
 - **Initial Review**: 
   - Steps for internal product team review.
@@ -81,9 +76,7 @@ PRD_PROMPT_TEMPLATE = """
 - **Final Approval**: 
   - Ensuring no surprises later on.
   - Agreement from all teams involved (UX design, engineering, QA).
-
 ---
-
 This PRD template is configured specifically for the user proposal: "{user_proposal}", ensuring a consistent format for every PRD, covering all necessary aspects from features to dependencies, review processes, and incorporating best practices in prompt engineering and task composability.
 """
 
@@ -131,11 +124,15 @@ def llm_call(user_prompt, system_prompt=None):
         {"role": "user", "content": user_prompt},
     ]
 
-    return client.chat.completions.create(
-        model="gpt-4-turbo-preview",
-        response_model=None,
-        messages=messages,
-    )
+    try:
+        return client.chat.completions.create(
+            model="gpt-4-turbo-preview",
+            response_model=None,
+            messages=messages,
+        )
+    except Exception as e:
+        logging.error(f"Error during LLM call: {e}")
+        return None
 
 # Define the Chainlit message handler
 @cl.on_chat_start
@@ -156,24 +153,31 @@ async def start():
         user_proposal = res['output']
 
         prd_sys1 = format_template(PRD_PROMPT_TEMPLATE, user_proposal=user_proposal) # system message to create PRD
-        prd_response_raw = llm_call(user_prompt=user_proposal, system_prompt=prd_sys1).choices[0].message.content # get PRD from LLM
-
-        # send PRD output to UI
-        prd_msg = cl.Message(content=prd_response_raw)
-        await prd_msg.send()
-
-        prd_json = json.dumps({"objective_goal": "Develop a chatbot...", "features": [], "ux_flow_design_notes": "...", "system_environment_requirements": "...", "assumptions": [], "constraints": [], "dependencies": [], "prompt_engineering_practices": "...", "task_composability": "...", "review_approval_process": "..."})
-        designer_prompt = format_template(DESIGNER_PROMPT_TEMPLATE, prd_response_raw=prd_response_raw, prd_json=prd_json, user_proposal=user_proposal)
-        designer_output = llm_call(designer_prompt).choices[0].message.content
-
-        designer_output_msg = cl.Message(content=designer_output)
-        await designer_output_msg.send()
-
-        # update outputs in UI
-        for secs in [1,5,10,20]:
-            await cl.sleep(secs)
-            await prd_msg.update()
-            await designer_output_msg.update()
-
-# Load the starters ... overrided by on_chat_start
-import starters
+        prd_response = llm_call(user_prompt=user_proposal, system_prompt=prd_sys1)
+        
+        if prd_response:
+            prd_response_raw = prd_response.choices[0].message.content
+
+            # send PRD output to UI
+            prd_msg = cl.Message(content=prd_response_raw)
+            await prd_msg.send()
+
+            prd_json = json.dumps({"objective_goal": "Develop a chatbot...", "features": [], "ux_flow_design_notes": "...", "system_environment_requirements": "...", "assumptions": [], "constraints": [], "dependencies": [], "prompt_engineering_practices": "...", "task_composability": "...", "review_approval_process": "..."})
+            designer_prompt = format_template(DESIGNER_PROMPT_TEMPLATE, prd_response_raw=prd_response_raw, prd_json=prd_json, user_proposal=user_proposal)
+            designer_response = llm_call(designer_prompt)
+
+            if designer_response:
+                designer_output = designer_response.choices[0].message.content
+
+                designer_output_msg = cl.Message(content=designer_output)
+                await designer_output_msg.send()
+
+                # update outputs in UI
+                for secs in [1, 5, 10, 20]:
+                    await cl.sleep(secs)
+                    await prd_msg.update()
+                    await designer_output_msg.update()
+            else:
+                await cl.Message(content="Error generating designer output. Please try again.").send()
+        else:
+            await cl.Message(content="Error generating PRD. Please