Create app.py

app.py

@@ -0,0 +1,189 @@
+from litellm import completion
+import os
+from flask import Flask, render_template, request, jsonify, session
+from flask_session import Session
+from datetime import datetime, timedelta
+
+os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY")
+
+app = Flask(__name__)
+app.secret_key = 'your_secret_key'
+
+app.config['SESSION_TYPE'] = 'filesystem'
+Session(app)
+
+prompt_dict = {}
+
+@app.route('/')
+def index():
+    return render_template('index.html', prompts=prompt_dict)
+
+@app.route('/add', methods=['POST'])
+def add_prompt():
+    prompt = request.form['prompt'].strip()
+    response = request.form['response'].strip()
+    if prompt and response:
+        prompt_dict[prompt] = response
+        flash('Prompt added successfully.')
+    else:
+        flash('Prompt or response cannot be empty.')
+    return redirect(url_for('index'))
+
+@app.route('/gpt3', methods=['POST'])
+def gpt3():
+    prompt = request.form['prompt']
+
+    # Initialize message_history in session if it doesn't exist
+    if 'message_history' not in session:
+        session['message_history'] = []
+
+    # Append the user input to message_history
+    session['message_history'].append("User: " + prompt)
+
+    def get_litellm_response(user_input, message_history):
+        # Convert message history to the format required by LiteLLM
+        messages = [{"role": msg.split(': ')[0].lower(), "content": msg.split(': ')[1]} for msg in message_history]
+
+        response = completion(
+            model="gpt-3.5-turbo",
+            messages=messages,
+            max_tokens=1200,
+            temperature=0.85,
+            n=1
+        )
+        return response['choices'][0]['message']['content']
+
+    try:
+        response_text = get_litellm_response(prompt, session['message_history'])
+        # Append the assistant's response to message_history
+        session['message_history'].append("Assistant: " + response_text.strip())
+        session.modified = True  # Ensure the session is saved after modification
+        return jsonify({"text": response_text.strip()})
+    except Exception as e:
+        return jsonify({"error": str(e)})
+
+@app.route('/super_coder', methods=['POST'])
+def super_coder():
+    super_coder_choice = request.form['super_coder_choice']
+
+    if super_coder_choice == "1":
+        app_name = request.form['app_name']
+        app_prompt = request.form['app_prompt']
+
+        # Initialize super_coder_history in session if it doesn't exist
+        if 'super_coder_history' not in session:
+            session['super_coder_history'] = []
+
+        session['super_coder_history'].append(f"New application {app_name} setup initiated.")
+
+        while True:
+            choice = request.form['choice']
+
+            if choice == "1":
+                session['super_coder_history'].append("Continuing development...")
+                response = get_litellm_response(f"Continue developing the {app_name} application based on the prompt: {app_prompt}", session['super_coder_history'])
+                session['super_coder_history'].append(response)
+            elif choice == "2":
+                guidance = request.form['guidance']
+                response = get_litellm_response(f"Provide guidance for the current development of {app_name}: {guidance}", session['super_coder_history'])
+                session['super_coder_history'].append(response)
+            elif choice == "3":
+                break
+            else:
+                session['super_coder_history'].append("Invalid choice. Please try again.")
+
+        session.modified = True
+        return jsonify({"history": session['super_coder_history']})
+
+    elif super_coder_choice == "2":
+        template_choice = request.form['template_choice']
+
+        template_instructions = {
+            "1": "Create a PyTorch application template with basic structure and dependencies.",
+            "2": "Create a machine learning pipeline template with data preprocessing, model training, and evaluation steps.",
+            "3": "Create a template that demonstrates the integration of Mergekit library for advanced functionality."
+        }
+
+        if template_choice in template_instructions:
+            instruction = template_instructions[template_choice]
+            response = get_litellm_response(instruction, [])
+            return jsonify({"text": response})
+        else:
+            return jsonify({"error": "Invalid choice. Please try again."})
+
+    elif super_coder_choice == "3":
+        prompt = request.form['prompt']
+        auto_steps = int(request.form['auto_steps'])
+        additional_steps = int(request.form['additional_steps'])
+
+        def autonomous_coding(prompt, steps, additional_steps):
+            history = []
+
+            if steps == 0:
+                while True:
+                    choice = request.form['choice']
+
+                    if choice == "1":
+                        history.append("Continuing development...")
+                        response = get_litellm_response(f"Continue developing the code based on the previous prompt: {prompt}", history)
+                        history.append(response)
+                    elif choice == "2":
+                        guidance = request.form['guidance']
+                        response = get_litellm_response(f"Provide guidance for the current development: {guidance}", history)
+                        history.append(response)
+                    elif choice == "3":
+                        break
+                    else:
+                        history.append("Invalid choice. Please try again.")
+            else:
+                for i in range(steps):
+                    history.append(f"Autonomous Coding Step {i+1}/{steps}")
+                    response = get_litellm_response(f"Continue developing the code autonomously based on the prompt: {prompt}", history)
+                    history.append(response)
+
+                if additional_steps > 0:
+                    for i in range(additional_steps):
+                        history.append(f"Additional Autonomous Coding Step {i+1}/{additional_steps}")
+                        response = get_litellm_response(f"Continue developing the code autonomously based on the prompt: {prompt}", history)
+                        history.append(response)
+
+                autonomous_coding(prompt, 0, 0)
+
+            return history
+
+        history = autonomous_coding(prompt, auto_steps, additional_steps)
+        return jsonify({"history": history})
+
+    elif super_coder_choice == "4":
+        # Placeholder for advanced settings
+        return jsonify({"text": "Advanced Settings functionality to be implemented."})
+
+    elif super_coder_choice == "5":
+        # Placeholder for managing prompt folder
+        return jsonify({"text": "Manage Prompt Folder functionality to be implemented."})
+
+    elif super_coder_choice == "6":
+        return jsonify({"text": "Returning to the main menu..."})
+
+    else:
+        return jsonify({"error": "Invalid choice. Please try again."})
+
+import json
+
+@app.route('/clear_session', methods=['GET'])
+def clear_session():
+    session.clear()
+    return jsonify({"result": "Session cleared"})
+
+@app.route('/history')
+def history():
+    if 'message_history' in session:
+        message_history = session['message_history']
+        # Convert the message history array to a JSON string
+        history_str = json.dumps(message_history)
+        return jsonify({"history": history_str})
+    else:
+        return jsonify({"history": "No message history found in the current session."})
+
+if __name__ == '__main__':
+    app.run(host='0.0.0.0', port=8080)