Delete app.py

app.py

@@ -1,279 +0,0 @@
-# --- PYTHON 3.13 COMPATIBILITY MONKEY-PATCH ---
-import sys
-import types
-
-if 'audioop' not in sys.modules:
-    mock_audioop = types.ModuleType('audioop')
-    mock_audioop.error = Exception
-    mock_audioop.getsample = lambda data, width, index: 0 
-    sys.modules['audioop'] = mock_audioop
-# ----------------------------------------------
-
-import os
-import sqlite3
-import pandas as pd
-import json
-import gradio as gr
-from groq import Groq
-import requests
-
-# --- Database Initialization ---
-DB_NAME = "strides_production.db"
-
-def init_db():
-    conn = sqlite3.connect(DB_NAME)
-    cursor = conn.cursor()
-    cursor.execute('''
-        CREATE TABLE IF NOT EXISTS project_tasks (
-            id INTEGER PRIMARY KEY AUTOINCREMENT,
-            timestamp DATETIME DEFAULT CURRENT_TIMESTAMP,
-            phase TEXT,
-            task_name TEXT,
-            owner TEXT,
-            timeline TEXT,
-            priority TEXT
-        )
-    ''')
-    
-    cursor.execute("SELECT COUNT(*) FROM project_tasks")
-    if cursor.fetchone()[0] == 0:
-        dummy_data = [
-            ('Discovery', 'Identify Deviation Root Cause', 'A. Sharma', 'Week 1', 'High'),
-            ('Data Prep', 'Clean LIMS Batch Records', 'R. Verma', 'Week 2-3', 'Medium'),
-            ('Modeling', 'Train Predictive Quality AI', 'S. Iyer', 'Week 4-5', 'High'),
-            ('Validation', 'Draft GxP Validation Protocol', 'M. Reddy', 'Week 6', 'High'),
-            ('Discovery', 'Audit Existing Logbook Manuals', 'P. Gupta', 'Week 1', 'Low'),
-            ('Data Prep', 'Standardize Sensor Timestamp Data', 'R. Verma', 'Week 2-3', 'Medium'),
-            ('Validation', 'Perform IQ/OQ/PQ for AI Model', 'M. Reddy', 'Week 6', 'High')
-        ]
-        cursor.executemany(
-            'INSERT INTO project_tasks (phase, task_name, owner, timeline, priority) VALUES (?,?,?,?,?)', 
-            dummy_data
-        )
-    conn.commit()
-    conn.close()
-
-init_db()
-
-# --- Dynamic Model Dropdown Refresher Logic ---
-GROQ_MODELS = [
-    "llama-3.1-8b-instant", 
-    "llama-3.1-70b-versatile", 
-    "openai/gpt-oss-20b", 
-    "whisper-large-v3-turbo"
-]
-
-OPENROUTER_MODELS = [
-    "nvidia/nemotron-3.5-content-safety:free",
-    "qwen/qwen3.7-plus",
-    "microsoft/mai-transcribe-1.5",
-    "google/gemini-embedding-2"
-]
-
-def update_model_dropdown(provider):
-    if provider == "Groq":
-        return gr.Dropdown(choices=GROQ_MODELS, value=GROQ_MODELS[0], label="Target Engine Architecture")
-    elif provider == "OpenRouter":
-        return gr.Dropdown(choices=OPENROUTER_MODELS, value=OPENROUTER_MODELS[0], label="Target Engine Architecture")
-
-# --- Common LLM API Request Orchestrator ---
-def call_llm(provider, api_key, model_choice, system_prompt, user_message, chat_history_format=None):
-    messages = [{"role": "system", "content": system_prompt}]
-    
-    if chat_history_format:
-        messages.extend(chat_history_format)
-    else:
-        messages.append({"role": "user", "content": user_message})
-
-    if provider == "Groq":
-        client = Groq(api_key=api_key)
-        groq_model_map = {
-            "llama-3.1-8b-instant": "llama-3.1-8b-instant",
-            "llama-3.1-70b-versatile": "llama-3.1-70b-versatile",
-            "openai/gpt-oss-20b": "llama-3.1-8b-instant",
-            "whisper-large-v3-turbo": "whisper-large-v3-turbo"
-        }
-        target_model = groq_model_map.get(model_choice, "llama-3.1-8b-instant")
-        response = client.chat.completions.create(
-            model=target_model, messages=messages, temperature=0.0
-        )
-        return response.choices[0].message.content.strip()
-
-    elif provider == "OpenRouter":
-        headers = {"Authorization": f"Bearer {api_key}", "Content-Type": "application/json"}
-        
-        # Maps user-facing selection directly to their OpenRouter global endpoints
-        openrouter_model_map = {
-            "nvidia/nemotron-3.5-content-safety:free": "nvidia/nemotron-3.5-content-safety:free",
-            "qwen/qwen3.7-plus": "qwen/qwen3.7-plus",
-            "microsoft/mai-transcribe-1.5": "microsoft/mai-transcribe-1.5",
-            "google/gemini-embedding-2": "google/gemini-embedding-2"
-        }
-        target_model = openrouter_model_map.get(model_choice, "nvidia/nemotron-3.5-content-safety:free")
-        
-        payload = {
-            "model": target_model, 
-            "messages": messages, 
-            "temperature": 0.0
-        }
-        response = requests.post("https://openrouter.ai/api/v1/chat/completions", headers=headers, json=payload)
-        if response.status_code == 200:
-            return response.json()['choices'][0]['message']['content'].strip()
-        else:
-            raise Exception(f"OpenRouter Gateway Alert ({response.status_code}): {response.text}")
-
-# --- Tab 1: Text-to-SQL Reader Desk Core Logic ---
-def execute_ai_query(provider, api_key, model_choice, user_question):
-    if not api_key.strip():
-        return None, "⚠️ Please enter a valid API Key to proceed."
-    if not user_question.strip():
-        return None, "⚠️ Please type a question or scenario."
-
-    system_prompt = (
-        "You are an expert SQL assistant. The database table is called 'project_tasks' with columns: "
-        "[id, timestamp, phase, task_name, owner, timeline, priority]. "
-        "Return ONLY a valid, executable raw SQLite query based on the user's question. "
-        "Do not include any explanation, introductory markdown, markdown code block wrappers, or trailing text. "
-        "Output raw text only."
-    )
-
-    try:
-        sql_query = call_llm(provider, api_key, model_choice, system_prompt, user_question)
-        sql_query = sql_query.replace("```sql", "").replace("```", "").replace("`", "").strip()
-
-        conn = sqlite3.connect(DB_NAME)
-        df = pd.read_sql_query(sql_query, conn)
-        conn.close()
-        return df, f"⚡ **Generated SQL Query:** `{sql_query}`"
-    except Exception as e:
-        return None, f"❌ Execution Failed: {str(e)}"
-
-# --- Tab 2: Conversation & Data Append Agent Core Logic ---
-def conversation_and_commit_agent(chat_history, provider, api_key, model_choice, user_msg):
-    if not api_key.strip():
-        chat_history.append({"role": "assistant", "content": "⚠️ Authentication missing. Please input your API Key on the left menu pane."})
-        return chat_history, ""
-    if not user_msg.strip():
-        return chat_history, ""
-
-    system_prompt = (
-        "You are a helpful conversational assistant and workflow coordinator for the Strides Pharma AI operational framework.\n"
-        "Your goal is twofold:\n"
-        "1. Respond to general conversational statements normally and professionally.\n"
-        "2. If the user expresses intent to log, add, or append a task milestone to the schedule, guide them or process it. "
-        "The required metrics are: Phase, Task_Name, Owner, Timeline, and Priority.\n\n"
-        "CRITICAL MECHANISM FOR DATA INSERTION:\n"
-        "If you have gathered or been explicitly provided structural task properties to log, you MUST append a raw JSON block at the very end of your final response text. "
-        "The format must exactly be: ||JSON_DATA: {\"phase\": \"...\", \"task_name\": \"...\", \"owner\": \"...\", \"timeline\": \"...\", \"priority\": \"...\"}||\n"
-        "If some schema details are completely absent, converse politely with the client to verify those remaining parameters before adding the string tag marker."
-    )
-
-    formatted_history = []
-    for turn in chat_history:
-        formatted_history.append({"role": turn["role"], "content": turn["content"]})
-    formatted_history.append({"role": "user", "content": user_msg})
-
-    try:
-        raw_response = call_llm(provider, api_key, model_choice, system_prompt, "", chat_history_format=formatted_history)
-        
-        cleaned_response = raw_response
-        database_committed_alert = ""
-        
-        if "||JSON_DATA:" in raw_response:
-            try:
-                parts = raw_response.split("||JSON_DATA:")
-                cleaned_response = parts[0].strip()
-                json_string = parts[1].replace("||", "").strip()
-                
-                data_payload = json.loads(json_string)
-                
-                conn = sqlite3.connect(DB_NAME)
-                cursor = conn.cursor()
-                cursor.execute(
-                    "INSERT INTO project_tasks (phase, task_name, owner, timeline, priority) VALUES (?, ?, ?, ?, ?)",
-                    (data_payload.get('phase'), data_payload.get('task_name'), data_payload.get('owner'), data_payload.get('timeline'), data_payload.get('priority'))
-                )
-                conn.commit()
-                conn.close()
-                
-                database_committed_alert = f"\n\n⚙️ **[SYSTEM UPDATE]:** Successfully appended task '{data_payload.get('task_name')}' to the internal server database master record structure."
-            except Exception as inner_err:
-                database_committed_alert = f"\n\n⚠️ **[SYSTEM NOTICE]:** Captured structure request token, but insertion execution aborted due to tracking parsing discrepancies: {str(inner_err)}"
-
-        final_display_text = cleaned_response + database_committed_alert
-        
-        chat_history.append({"role": "user", "content": user_msg})
-        chat_history.append({"role": "assistant", "content": final_display_text})
-        return chat_history, ""
-
-    except Exception as e:
-        chat_history.append({"role": "user", "content": user_msg})
-        chat_history.append({"role": "assistant", "content": f"❌ API Connection Failure: {str(e)}"})
-        return chat_history, ""
-
-# --- Interface Layout Configuration ---
-with gr.Blocks(theme=gr.themes.Soft()) as demo:
-    gr.Markdown("# 💊 Strides Pharma AI Production & Data Portal")
-    gr.Markdown("Orchestrate production tasks, converse with core system workflows, and modify infrastructure records securely.")
-    
-    with gr.Row():
-        with gr.Column(scale=1):
-            gr.Markdown("### 🔑 Global Control Tower")
-            provider_select = gr.Dropdown(choices=["Groq", "OpenRouter"], value="Groq", label="API Gateway Provider")
-            token_input = gr.Textbox(label="User API Secret Key", type="password", placeholder="gsk_... or sk-or-...")
-            
-            # This component gets updated dynamically by the event handler below
-            model_select = gr.Dropdown(
-                choices=GROQ_MODELS, 
-                value=GROQ_MODELS[0], 
-                label="Target Engine Architecture"
-            )
-            
-            gr.Markdown("✨ **Global Platform Database State Schema:**\n- `phase` (Discovery, Data Prep, Modeling, Validation)\n- `task_name` (Structural workflow description string)\n- `owner` (Assigned personnel scientist name)\n- `timeline` (Expected operational delivery time frames)\n- `priority` (High, Medium, Low)")
-            
-        with gr.Column(scale=2):
-            with gr.Tabs():
-                
-                with gr.TabItem("🤖 Interactive Data Contributor Chatbot"):
-                    gr.Markdown("### Conversational Contributor Agent")
-                    gr.Markdown("Chat with this engine normally, or tell it to log a brand-new task assignment milestone directly into the active SQLite infrastructure.")
-                    
-                    chatbot_viewport = gr.Chatbot(type="messages", label="Operational History Workspace")
-                    chat_input = gr.Textbox(placeholder="Say hello, or submit task details to log...", label="Your Message")
-                    send_btn = gr.Button("Submit Message", variant="primary")
-                    
-                    send_btn.click(
-                        fn=conversation_and_commit_agent,
-                        inputs=[chatbot_viewport, provider_select, token_input, model_select, chat_input],
-                        outputs=[chatbot_viewport, chat_input]
-                    )
-                    chat_input.submit(
-                        fn=conversation_and_commit_agent,
-                        inputs=[chatbot_viewport, provider_select, token_input, model_select, chat_input],
-                        outputs=[chatbot_viewport, chat_input]
-                    )
-                
-                with gr.TabItem("🔎 SQL Inquisitor Desk"):
-                    gr.Markdown("### Natural Language SQL Query Engine")
-                    query_input = gr.Textbox(label="Query the current database contents using conversational English:", placeholder="e.g., Show me all records sorted by priority status")
-                    query_btn = gr.Button("Evaluate Infrastructure", variant="secondary")
-                    
-                    sql_status_display = gr.Markdown()
-                    output_data_table = gr.DataFrame(label="Queried Records Dataframe Live Output")
-                    
-                    query_btn.click(
-                        fn=execute_ai_query,
-                        inputs=[provider_select, token_input, model_select, query_input],
-                        outputs=[output_data_table, sql_status_display]
-                    )
-
-    # --- REACTIVE EVENT LISTENER ---
-    # Whenever the provider dropdown changes, change the choices of the model select dropdown
-    provider_select.change(
-        fn=update_model_dropdown,
-        inputs=[provider_select],
-        outputs=[model_select]
-    )
-
-if __name__ == "__main__":
-    demo.launch()