Upload 2 files

app.py

@@ -1,6 +1,13 @@
 """
 Python to C++ Code Optimizer - Modern UI with Password Protection
-AI-powered code conversion using GPT-4o and Claude-3.5-Sonnet
+AI-powered code conversion using GPT-4o, Claude-3.5-Sonnet, and Open Source models
+
+Supported Models:
+- GPT-4o (OpenAI) - Premium, fastest, most accurate
+- Claude-3.5-Sonnet (Anthropic) - Premium, excellent for code
+- CodeLlama-34B (Meta) - Open source, free/cheap
+- DeepSeek-Coder-33B - Open source, excellent for code
+- Mistral-7B - Open source, fast, general purpose
 
 ⚠️ SECURITY WARNING:
 This app executes arbitrary code. Only run code from trusted sources.
@@ -12,6 +19,8 @@ import io
 import sys
 import subprocess
 import socket
+import requests
+import httpx
 from openai import OpenAI
 import anthropic
 import gradio as gr
@@ -27,23 +36,47 @@ except ImportError:
 # Set this as a Hugging Face Secret: APP_PASSWORD
 APP_PASSWORD = os.environ.get("APP_PASSWORD", "demo123")  # Change default!
 
-# Lazy initialization of AI clients
+# Lazy initialization of AI clients with explicit HTTP client to avoid Gradio conflicts
 def get_openai_client():
     api_key = os.environ.get("OPENAI_API_KEY")
     if not api_key:
         raise ValueError("OPENAI_API_KEY not found. Please set it in your environment or .env file.")
-    return OpenAI(api_key=api_key)
+    
+    # Create a clean HTTP client without proxies to avoid Gradio conflicts
+    http_client = httpx.Client(
+        timeout=60.0,
+        limits=httpx.Limits(max_keepalive_connections=5, max_connections=10)
+    )
+    
+    return OpenAI(api_key=api_key, http_client=http_client)
 
 def get_claude_client():
     api_key = os.environ.get("ANTHROPIC_API_KEY")
     if not api_key:
         raise ValueError("ANTHROPIC_API_KEY not found. Please set it in your environment or .env file.")
-    return anthropic.Anthropic(api_key=api_key)
+    
+    # Create a clean HTTP client without proxies to avoid Gradio conflicts
+    http_client = httpx.Client(
+        timeout=60.0,
+        limits=httpx.Limits(max_keepalive_connections=5, max_connections=10)
+    )
+    
+    return anthropic.Anthropic(api_key=api_key, http_client=http_client)
 
 # Model configurations
 OPENAI_MODEL = "gpt-4o"
 CLAUDE_MODEL = "claude-3-5-sonnet-20240620"
 
+# Hugging Face models (open source)
+HF_MODELS = {
+    "CodeLlama-34B": "codellama/CodeLlama-34b-Instruct-hf",
+    "DeepSeek-Coder-33B": "deepseek-ai/deepseek-coder-33b-instruct",
+    "Mistral-7B": "mistralai/Mistral-7B-Instruct-v0.2"
+}
+
+# Hugging Face API endpoint
+HF_API_URL = "https://api-inference.huggingface.co/models/"
+
 # System and user prompts
 system_message = (
     "You are an assistant that reimplements Python code in high performance C++. "
@@ -111,12 +144,68 @@ def stream_claude(python):
     except Exception as e:
         yield f"❌ Error: {str(e)}"
 
+def stream_huggingface(python, model_name):
+    """Stream Hugging Face model response"""
+    try:
+        # Get HF token (optional - works without it but with rate limits)
+        hf_token = os.environ.get("HF_TOKEN", "")
+        
+        # Get the model ID
+        model_id = HF_MODELS.get(model_name)
+        if not model_id:
+            yield f"❌ Unknown model: {model_name}"
+            return
+        
+        headers = {"Authorization": f"Bearer {hf_token}"} if hf_token else {}
+        
+        # Prepare the prompt
+        prompt = f"{system_message}\n\n{user_prompt_for(python)}"
+        
+        payload = {
+            "inputs": prompt,
+            "parameters": {
+                "max_new_tokens": 2000,
+                "temperature": 0.7,
+                "return_full_text": False
+            }
+        }
+        
+        # Call HF Inference API
+        response = requests.post(
+            HF_API_URL + model_id,
+            headers=headers,
+            json=payload,
+            timeout=60
+        )
+        
+        if response.status_code == 200:
+            result = response.json()
+            if isinstance(result, list) and len(result) > 0:
+                generated_text = result[0].get("generated_text", "")
+            else:
+                generated_text = result.get("generated_text", "")
+            
+            # Clean up the response
+            reply = generated_text.replace('```cpp\n','').replace('```','')
+            yield reply
+        else:
+            error_msg = response.json().get("error", "Unknown error")
+            if "loading" in error_msg.lower():
+                yield f"⏳ Model is loading... This may take 20-30 seconds. Please try again."
+            else:
+                yield f"❌ Error from Hugging Face: {error_msg}"
+            
+    except Exception as e:
+        yield f"❌ Error calling Hugging Face: {str(e)}"
+
 def optimize(python, model):
     """Convert Python to C++ using selected AI model"""
     if model in ["GPT-4o", "GPT"]:
         result = stream_gpt(python)
     elif model in ["Claude-3.5-Sonnet", "Claude"]:
         result = stream_claude(python)
+    elif model in HF_MODELS.keys():
+        result = stream_huggingface(python, model)
     else:
         raise ValueError(f"Unknown model: {model}")
     
@@ -391,7 +480,7 @@ def create_interface():
                 
                 gr.Markdown("### 🤖 AI Model Selection")
                 model_selector = gr.Dropdown(
-                    ["GPT-4o", "Claude-3.5-Sonnet"], 
+                    ["GPT-4o", "Claude-3.5-Sonnet", "CodeLlama-34B", "DeepSeek-Coder-33B", "Mistral-7B"], 
                     label="Select AI Model", 
                     value="GPT-4o",
                     elem_classes=["model-selector"]

requirements.txt

@@ -1,6 +1,7 @@
 gradio==4.44.1
-openai==1.54.4
-anthropic==0.39.0
+openai==1.55.0
+anthropic==0.40.0
 python-dotenv==1.0.0
-httpx>=0.24.0
+requests>=2.31.0
+httpx>=0.27.0