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app.py

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+import gradio as gr
+from huggingface_hub import InferenceClient
+import json
+import random
+import os
+
+# Initialize the client (uses HUGGING_FACE_HUB_TOKEN from environment)
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+SYSTEM_PROMPT = """You are ModelForge, an expert AI architecture assistant. Your goal is to analyze machine learning problems and generate detailed, deployable solutions in strict JSON format.
+
+You must analyze the user's request and return a JSON object with the following structure:
+{
+  "analysis": {
+    "dataType": "image" | "text" | "tabular" | "audio" | "video" | "time_series" | "multimodal",
+    "taskType": "classification" | "regression" | "nlp" | "vision" | "forecasting" | "multimodal_reasoning",
+    "complexity": "low" | "medium" | "high" | "research",
+    "domain": "string (e.g., medical, finance, etc.)"
+  },
+  "recommendations": [
+    {
+      "name": "Model Name",
+      "description": "Detailed technical description...",
+      "pros": ["pro1", "pro2", "pro3"],
+      "cons": ["con1", "con2"],
+      "architectureDiagram": "Mermaid graph definition...",
+      "mlopsBestPractices": ["step 1", "step 2", ...],
+      "trainingCode": "Python code snippet..."
+    }
+  ]
+}
+
+Provide 2-3 distinct recommendations. For research-level problems, propose novel architectures.
+Ensure the Mermaid diagram uses valid syntax (no curly braces for nodes, use square brackets []).
+"""
+
+FEW_SHOT_EXAMPLES = """
+Example Input: "Detect fraud in credit card transactions"
+Example Output:
+{
+  "analysis": { "dataType": "tabular", "taskType": "classification", "complexity": "medium", "domain": "finance" },
+  "recommendations": [
+    {
+      "name": "XGBoost Fraud Detector",
+      "description": "Gradient boosting ensemble optimized for imbalanced tabular data...",
+      "pros": ["High interpretability", "Handles missing data"],
+      "cons": ["Feature engineering required"],
+      "architectureDiagram": "graph TD\\nA[Raw Data] --> B[Preprocessing]\\nB --> C[XGBoost]",
+      "mlopsBestPractices": ["Use DVC for data", "Monitor drift"],
+      "trainingCode": "import xgboost as xgb..."
+    }
+  ]
+}
+"""
+
+def generate_solution(description):
+    prompt = f"{SYSTEM_PROMPT}\n\n{FEW_SHOT_EXAMPLES}\n\nUser Input: \"{description}\"\n\nJSON Response:"
+    
+    try:
+        response = client.text_generation(
+            prompt,
+            max_new_tokens=2048,
+            temperature=0.7,
+            top_p=0.95,
+            return_full_text=False
+        )
+        
+        # clean up response to ensure it's valid JSON
+        json_str = response.strip()
+        if json_str.startswith("```json"):
+            json_str = json_str.split("```json")[1].split("```")[0].strip()
+        elif json_str.startswith("```"):
+             json_str = json_str.split("```")[1].split("```")[0].strip()
+             
+        return json_str
+    except Exception as e:
+        return json.dumps({
+            "error": str(e),
+            "analysis": {"dataType": "text", "taskType": "nlp", "complexity": "low", "domain": "general"},
+            "recommendations": []
+        })
+
+demo = gr.Interface(
+    fn=generate_solution,
+    inputs=gr.Textbox(lines=5, placeholder="Describe your ML problem..."),
+    outputs=gr.JSON(label="Recommendations"),
+    title="ModelForge AI Backend",
+    description="Generates ML recommendations via LLM."
+)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio
+huggingface_hub