Update app.py

app.py

@@ -1,64 +1,222 @@
 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")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    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 = ""
-
-    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
-"""
-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)",
-        ),
-    ],
-)
-
-
-if __name__ == "__main__":
-    demo.launch()
+from gradio import ChatMessage
+import time
+import asyncio
+from functools import partial
+import random
+import logging
+
+logging.basicConfig(level=logging.INFO)
+
+sleep_time = random.randint(1, 3)
+
+thoughts = {
+  "questioning_agent": [
+    "Read the project document and function list.",
+    "Determine if the project needs a chatbot, document extraction, or both.",
+    "Map the requirements to the provided functions only.",
+    "Classify the project as Chatbot, Document Extraction, or Hybrid.",
+    "Output a JSON object with the configuration type and selected functions."
+  ],
+  "client_initial_question": [
+    "Identify key topics like company background, industry, challenges, and workflows.",
+    "List the specific client questions provided.",
+    "Ensure each question aims to gather clear, measurable information.",
+    "Format the questions with sample answers as specified.",
+    "Return only the list of questions without extra commentary."
+  ],
+  "generate_client_follow_up": [
+    "Review the initial client responses.",
+    "Pinpoint areas needing further clarification, such as project vision and current processes.",
+    "Develop follow-up questions to explore these areas in more detail.",
+    "Incorporate sample answers to guide the client.",
+    "Compile a numbered list of the top follow-up questions."
+  ],
+  "generate_engage_questions": [
+    "Examine the client background and chatbot requirements.",
+    "Focus on areas like business outcomes, conversational flow, and technical needs.",
+    "Formulate context-aware questions to extract detailed insights.",
+    "Include sample answers for clarity.",
+    "Present a concise list of targeted questions."
+  ],
+  "generate_page_questions": [
+    "Review the client information related to document processing.",
+    "Focus on document types, input/output formats, quality, and workflow mapping.",
+    "Develop clear and relevant questions for each area.",
+    "Provide sample answers to guide responses.",
+    "Return a formatted list of document-focused questions."
+  ],
+  "generate_hybrid_questions": [
+    "Recognize that the project involves both chatbot and document processing needs.",
+    "Separate the questions into two groups: one for documents and one for chatbots.",
+    "Develop targeted questions for each group using the client context.",
+    "Add sample answers to provide clarity.",
+    "Combine both sets into one cohesive list."
+  ],
+  "generate_general_questions": [
+    "Review the overall client background and project requirements.",
+    "Identify key areas such as integration, performance, and security.",
+    "Craft context-aware questions that are precise and actionable.",
+    "Include sample answers to illustrate expected responses.",
+    "Return a clear list of general questions."
+  ],
+  "generate_further_follow_up_questions": [
+    "Examine the client background and previous responses in detail.",
+    "Identify any gaps or unclear points needing further detail.",
+    "Formulate direct follow-up questions using techniques like the 5 Whys.",
+    "Reference prior responses to maintain context.",
+    "List each follow-up question with sample answers for guidance."
+  ]
+}
+
+async def client_initial_question():
+    """Return client information gathering questions."""
+    return """
+    # Client Information Gathering Questions
+        
+    ### Company Background and Industry
+    1. Can you provide some background about your company?
+    2. Which industry do you operate in, and what is your company's niche or specialization?
+    3. Who are your primary customers?
+    4. What are the main objectives you want to achieve?
+    5. What key features or functionalities do you need?
+        
+    ### Current Challenges
+    6. What are the biggest challenges your firm is currently facing?
+    7. Can you describe your current processes?
+        
+    ### Workflow and System Impact
+    8. How will this solution benefit your firm as a whole?
+        
+    ### Existing Workflow or System
+    9. Can you describe your current workflow or system?
+        
+    ### Pain Point Identification
+    10. Where is your current system falling short or causing delays?
+    11. Are there any parts of the process that are particularly time-consuming/ prone to error?
+    """
+
+async def simulate_thinking_chat(message, history):
+    logging.info(f"Received message: {message}")
+    logging.info(f"Initial history: {history}")
+    start_time = time.time()
+    response = ChatMessage(
+        content="",
+        metadata={"title": "_Processing_ step-by-step", "id": 0, "status": "pending"}
+    )
+    yield response, ""
+
+    # Determine which function to call based on history length
+    if len(history) == 0:
+        function_name = "client_initial_question"
+        current_thoughts = thoughts["client_initial_question"]
+        async_func = client_initial_question()
+    elif len(history) <= 3:  # Overlapping condition for initial questions
+        function_name = "generate_general_questions"
+        current_thoughts = thoughts["generate_general_questions"]
+        async_func = generate_general_questions()  # You'll need to implement this
+    elif len(history) <= 6:  # Overlapping condition for general questions
+        function_name = "generate_further_follow_up_questions"
+        current_thoughts = thoughts["generate_further_follow_up_questions"]
+        async_func = generate_further_follow_up_questions()  # You'll need to implement this
+    else:
+        # Default to client initial questions if no other case matches
+        function_name = "client_initial_question"
+        current_thoughts = thoughts["client_initial_question"]
+        async_func = client_initial_question()
+
+    # Create a task for getting the appropriate response
+    response_task = asyncio.create_task(async_func)
+    
+    # Show thoughts from the global thoughts dictionary
+    accumulated_thoughts = ""
+    thought_index = 0
+    while not response_task.done():
+        thought = current_thoughts[thought_index % len(current_thoughts)]
+        thought_index += 1
+        
+        await asyncio.sleep(sleep_time)
+        accumulated_thoughts += f"- {thought}\n\n"
+        response.content = accumulated_thoughts.strip()
+        yield response, ""
+
+    # Get the result from the completed task
+    result = await response_task
+
+    response.metadata["status"] = "done"
+    response.metadata["duration"] = time.time() - start_time
+    yield response, ""
+
+    response_list = [
+        response,
+        ChatMessage(content=result)
+    ]
+    print(f"Function: {function_name}\nMessage: {message},\nLen: {len(history)},\nHistory: {history}")
+
+    yield response_list, result
+
+# Add new async functions for the additional question types
+async def generate_general_questions():
+    await asyncio.sleep(10)  # Convert to async sleep
+    return """
+    # General Integration and Deployment Questions
+    
+    1. What are your current system integrations?
+       Sample: "We use Salesforce for CRM and SAP for ERP"
+    
+    2. What are your security requirements?
+       Sample: "We need SSO integration and data encryption at rest"
+    
+    3. What is your expected deployment timeline?
+       Sample: "We aim to go live within 3 months"
+    
+    4. Do you have any specific performance requirements?
+       Sample: "System should handle 1000 concurrent users"
+    
+    5. What is your preferred hosting environment?
+       Sample: "We prefer AWS cloud hosting"
+    """
+
+async def generate_further_follow_up_questions():
+    await asyncio.sleep(10)  # Convert to async sleep
+    return """
+    # Follow-up Questions Based on Previous Responses
+    
+    1. Could you elaborate on your current workflow bottlenecks?
+       Sample: "Manual data entry takes 4 hours daily"
+    
+    2. What specific metrics would indicate project success?
+       Sample: "50% reduction in processing time"
+    
+    3. Have you identified any potential risks or challenges?
+       Sample: "Data migration from legacy systems"
+    
+    4. What is your expected ROI timeframe?
+       Sample: "We expect to see returns within 6 months"
+    
+    5. Are there any compliance requirements we should be aware of?
+       Sample: "We need to comply with GDPR and HIPAA"
+    """
+
+chatbot = gr.Chatbot(height=650 ,elem_classes=["chatbot-container"], label="Project Questions")
+
+with gr.Blocks(fill_height=True) as demo:
+    with gr.Row():
+        with gr.Column(scale=1):
+            # output = gr.Textbox(label="Output")
+            current_question = gr.Textbox(label="Edit Area", lines=30, interactive=True)
+            # submit_btn = gr.Button("Submit")
+            # clear_btn = gr.Button("Clear Chat")
+        with gr.Column(scale=1):
+            gr.ChatInterface(
+                simulate_thinking_chat,
+                chatbot= chatbot,
+                type="messages",
+                fill_height=True,
+                additional_outputs= [current_question],
+                flagging_mode= "manual"
+                # show_progress= 'minimal',
+                # save_history= True
+            )
+
+demo.launch()