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app.py

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+import gradio as gr
+from transformers import pipeline
+from flask import Flask, request, jsonify
+import os
+from huggingface_hub import login
+
+# Retrieve the Hugging Face token from environment variables
+HF_TOKEN = os.getenv("HUGGINGFACE_TOKEN")
+
+if HF_TOKEN is None:
+    raise ValueError("Hugging Face token is not set in environment variables")
+
+# Authenticate with Hugging Face
+try:
+    login(token=HF_TOKEN, add_to_git_credential=True)
+except ValueError as e:
+    print(f"Error during login: {e}")
+    raise
+
+# Load your model and tokenizer
+model_id = "rish13/llm_for_advanced_materials"  # Replace with your model repo ID
+model = pipeline('text-generation', model=model_id)
+
+# Define Gradio interface
+def generate_text(prompt):
+    return model(prompt)[0]['generated_text']
+
+gradio_interface = gr.Interface(fn=generate_text, inputs="text", outputs="text")
+
+# Initialize Flask app
+app = Flask(__name__)
+
+@app.route('/search', methods=['POST'])
+def predict_endpoint():
+    data = request.json
+    prompt = data.get('prompt', '')
+    generated_text = generate_text(prompt)
+    return jsonify({"result": generated_text})
+
+@app.route('/')
+def home():
+    return "Welcome to the text generation API. Use the /search endpoint to generate text."
+
+if __name__ == "__main__":
+    # Run the Gradio interface in a separate thread
+    from threading import Thread
+    gradio_thread = Thread(target=lambda: gradio_interface.launch(share=False, inbrowser=True))
+    gradio_thread.start()
+    
+    # Run the Flask app
+    app.run(host='0.0.0.0', port=5000)

requirements.txt

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+Flask
+gradio
+transformers
+huggingface_hub
+torch
+python-dotenv