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.env.example

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+# Optional environment variables for Medini Space
+# HF_TOKEN=your_huggingface_token_here

README.md

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+---
+title: Medini AI Space
+emoji: 🤖
+colorFrom: blue
+colorTo: purple
+sdk: gradio
+sdk_version: "3.39.0"
+app_file: app.py
+pinned: false
+---
+
+# Medini AI Space
+
+## Overview
+Medini AI is a generative AI application built on Hugging Face Spaces. It uses a text generation model (`PuruAI/Medini_Intelligence`) and a sentence embedding model (`all-MiniLM-L6-v2`) to provide AI-generated responses to user prompts.
+
+## Features
+- Interactive Gradio interface.
+- Public embedding model, no authentication token required.
+- Fallback to GPT-2 if main model fails.
+- Logging for debugging and monitoring.
+
+## How to Run
+1. Ensure all dependencies are installed:
+```bash
+pip install -r requirements.txt
+```
+2. Launch the app:
+```bash
+python app.py
+```
+3. Open the URL provided by Gradio to interact with the AI.
+
+## Notes
+- No Hugging Face token is required for public model access.
+- Handles model load failures gracefully.
+- Recommended to deploy on Hugging Face Spaces for easy hosting.

app.py

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+import gradio as gr
+from transformers import pipeline
+from sentence_transformers import SentenceTransformer
+import logging
+
+# Logging setup
+logging.basicConfig(level=logging.INFO)
+logger = logging.getLogger(__name__)
+
+# Model configuration
+MODEL_ID = "PuruAI/Medini_Intelligence"
+FALLBACK_MODEL = "gpt2"
+EMB_MODEL = "sentence-transformers/all-MiniLM-L6-v2"
+
+# Load embedding model
+try:
+    logger.info(f"Loading embedding model: {EMB_MODEL}")
+    embedding_model = SentenceTransformer(EMB_MODEL)
+    logger.info("Embedding model loaded successfully.")
+except Exception as e:
+    logger.error(f"Failed to load embedding model: {e}")
+    embedding_model = None
+
+# Load main LLM
+try:
+    logger.info(f"Loading main model: {MODEL_ID}")
+    generator = pipeline("text-generation", model=MODEL_ID)
+    logger.info("Main model loaded successfully.")
+except Exception as e:
+    logger.warning(f"Failed to load {MODEL_ID}, falling back to {FALLBACK_MODEL}: {e}")
+    generator = pipeline("text-generation", model=FALLBACK_MODEL)
+
+# Gradio interface
+def generate_text(prompt):
+    try:
+        result = generator(prompt, max_length=200)
+        return result[0]["generated_text"]
+    except Exception as e:
+        logger.error(f"Text generation failed: {e}")
+        return "Error generating text."
+
+with gr.Blocks() as demo:
+    gr.Markdown("## Medini AI Space")
+    user_input = gr.Textbox(label="Enter your prompt")
+    output = gr.Textbox(label="Generated Text")
+    submit = gr.Button("Generate")
+    submit.click(generate_text, inputs=user_input, outputs=output)
+
+if __name__ == "__main__":
+    demo.launch()

requirements.txt

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+gradio==3.39.0
+transformers==4.42.2
+sentence-transformers>=2.2.2,<3
+torch>=2.0.0
+huggingface_hub>=0.14.1

start.sh

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+#!/bin/bash
+
+# Force reinstall dependencies to ensure correct versions
+pip install --upgrade --force-reinstall -r requirements.txt
+
+# Launch the app
+python app.py