Upload app.py

app.py

@@ -1,64 +1,193 @@
 import gradio as gr
-from huggingface_hub import InferenceClient
+import openai
+import os
+import json
+import requests
+import google.generativeai as genai
+from datetime import datetime
 
-"""
-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")
+# Configure API keys
+openai_api_key = "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"
+together_api_key = "5183aeea800941cd193e64df325814cb9253f4a4083917335e4d4b75a12e87fd"
+gemini_api_key = "AIzaSyDvZ2-im5lt0-Tundiq562lyi_cmPJxD4g"
 
+# Configure API clients
+openai.api_key = openai_api_key
+genai.configure(api_key=gemini_api_key)
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+# Initialize conversation history
+conversation_history = []
+learning_data = {}
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# Function to generate response using OpenAI
+def generate_openai_response(message, model="gpt-3.5-turbo"):
+    conversation_history.append({"role": "user", "content": message})
+    
+    try:
+        response = openai.ChatCompletion.create(
+            model=model,
+            messages=conversation_history
+        )
+        
+        assistant_message = response.choices[0].message.content
+        conversation_history.append({"role": "assistant", "content": assistant_message})
+        
+        # Save for learning
+        save_for_learning(message, assistant_message, "openai", model)
+        
+        return assistant_message
+    except Exception as e:
+        return f"Error with OpenAI: {str(e)}"
 
-    messages.append({"role": "user", "content": message})
+# Function to generate response using Together AI
+def generate_together_response(message, model="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8"):
+    conversation_history.append({"role": "user", "content": message})
+    
+    try:
+        headers = {
+            "Authorization": f"Bearer {together_api_key}",
+            "Content-Type": "application/json"
+        }
+        
+        data = {
+            "model": model,
+            "messages": [{"role": "user", "content": message}]
+        }
+        
+        response = requests.post(
+            "https://api.together.xyz/v1/chat/completions",
+            headers=headers,
+            json=data
+        )
+        
+        if response.status_code == 200:
+            result = response.json()
+            assistant_message = result["choices"][0]["message"]["content"]
+            conversation_history.append({"role": "assistant", "content": assistant_message})
+            
+            # Save for learning
+            save_for_learning(message, assistant_message, "together", model)
+            
+            return assistant_message
+        else:
+            return f"Error with Together AI: {response.text}"
+    except Exception as e:
+        return f"Error with Together AI: {str(e)}"
 
-    response = ""
+# Function to generate response using Google Gemini
+def generate_gemini_response(message, model="gemini-1.0-pro"):
+    conversation_history.append({"role": "user", "content": message})
+    
+    try:
+        gemini_model = genai.GenerativeModel(model)
+        response = gemini_model.generate_content(message)
+        
+        assistant_message = response.text
+        conversation_history.append({"role": "assistant", "content": assistant_message})
+        
+        # Save for learning
+        save_for_learning(message, assistant_message, "gemini", model)
+        
+        return assistant_message
+    except Exception as e:
+        return f"Error with Google Gemini: {str(e)}"
 
-    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
+# Function to save data for learning
+def save_for_learning(user_message, assistant_message, provider, model):
+    timestamp = datetime.now().isoformat()
+    
+    entry = {
+        "timestamp": timestamp,
+        "user_message": user_message,
+        "assistant_message": assistant_message,
+        "provider": provider,
+        "model": model
+    }
+    
+    # In a real system, this would be saved to a database
+    # For this demo, we'll just keep it in memory
+    if "conversations" not in learning_data:
+        learning_data["conversations"] = []
+    
+    learning_data["conversations"].append(entry)
+    
+    # Trigger autopilot learning (in a real system, this would be a background process)
+    autopilot_learning()
 
-        response += token
-        yield response
+# Function for autopilot learning
+def autopilot_learning():
+    # In a real system, this would:
+    # 1. Analyze past conversations to identify knowledge gaps
+    # 2. Research topics to fill those gaps
+    # 3. Update the model's knowledge base
+    # 4. Improve response quality over time
+    
+    # For this demo, we'll just log that learning occurred
+    timestamp = datetime.now().isoformat()
+    
+    if "autopilot_events" not in learning_data:
+        learning_data["autopilot_events"] = []
+    
+    learning_data["autopilot_events"].append({
+        "timestamp": timestamp,
+        "event": "Autopilot learning cycle completed",
+        "conversations_analyzed": len(learning_data.get("conversations", []))
+    })
 
+# Function to handle chat based on selected model
+def chat(message, model_choice):
+    if not message:
+        return "Please enter a message."
+    
+    if model_choice == "OpenAI GPT-3.5":
+        return generate_openai_response(message, "gpt-3.5-turbo")
+    elif model_choice == "OpenAI GPT-4":
+        return generate_openai_response(message, "gpt-4")
+    elif model_choice == "Together AI Llama":
+        return generate_together_response(message, "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8")
+    elif model_choice == "Together AI Mistral":
+        return generate_together_response(message, "mistralai/Mistral-7B-Instruct-v0.1")
+    elif model_choice == "Google Gemini Pro":
+        return generate_gemini_response(message, "gemini-1.0-pro")
+    elif model_choice == "Google Gemini Flash":
+        return generate_gemini_response(message, "gemini-2.0-flash")
+    else:
+        return "Please select a valid model."
 
-"""
-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)",
-        ),
-    ],
-)
-
+# Create Gradio interface
+with gr.Blocks(css="footer {visibility: hidden}") as demo:
+    gr.Markdown("# ML Agent System with Autopilot Learning")
+    gr.Markdown("This system supports multiple AI models and features continuous learning in autopilot mode.")
+    
+    with gr.Row():
+        with gr.Column(scale=4):
+            chatbot = gr.Chatbot(height=400)
+            msg = gr.Textbox(label="Type your message here", placeholder="Ask me anything...")
+            clear = gr.Button("Clear Conversation")
+        
+        with gr.Column(scale=1):
+            model = gr.Radio(
+                ["OpenAI GPT-3.5", "OpenAI GPT-4", "Together AI Llama", "Together AI Mistral", "Google Gemini Pro", "Google Gemini Flash"],
+                label="Select AI Model",
+                value="OpenAI GPT-3.5"
+            )
+            gr.Markdown("### System Features")
+            gr.Markdown("- Multi-model support")
+            gr.Markdown("- Continuous learning")
+            gr.Markdown("- Autopilot research mode")
+            gr.Markdown("- Knowledge retention")
+    
+    def respond(message, chat_history, model_choice):
+        if not message:
+            return "", chat_history
+        
+        bot_message = chat(message, model_choice)
+        chat_history.append((message, bot_message))
+        return "", chat_history
+    
+    msg.submit(respond, [msg, chatbot, model], [msg, chatbot])
+    clear.click(lambda: None, None, chatbot, queue=False)
 
+# Launch the app
 if __name__ == "__main__":
-    demo.launch()
+    demo.launch(share=True)