Update app.py

app.py

@@ -1,3 +1,5 @@
+import gradio as gr
+from huggingface_hub import InferenceClient
 from flask import Flask, render_template, request, jsonify
 from simple_salesforce import Salesforce
 from dotenv import load_dotenv
@@ -11,9 +13,10 @@ load_dotenv()
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
 
+# Initialize Flask app
 app = Flask(__name__, template_folder='templates', static_folder='static')
 
-# Function to get Salesforce connection
+# Initialize Salesforce connection
 def get_salesforce_connection():
     try:
         sf = Salesforce(
@@ -28,9 +31,60 @@ def get_salesforce_connection():
         logger.error(f"Error connecting to Salesforce: {e}")
         return None
 
-# Initialize Salesforce connection
-sf = get_salesforce_connection()
+# Connect to HuggingFace model
+client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+
+# Define Gradio respond function for chatbot
+def respond(
+    message,
+    history: list[tuple[str, str]],
+    system_message,
+    max_tokens,
+    temperature,
+    top_p,
+):
+    messages = [{"role": "system", "content": system_message}]
+
+    for val in history:
+        if val[0]:
+            messages.append({"role": "user", "content": val[0]})
+        if val[1]:
+            messages.append({"role": "assistant", "content": val[1]})
+
+    messages.append({"role": "user", "content": message})
 
+    response = ""
+
+    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
+
+        response += token
+        yield response
+
+# Create Gradio chat interface
+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)",
+        ),
+    ],
+)
+
+# Flask routes
 @app.route('/')
 def index():
     return render_template('index.html')
@@ -72,5 +126,10 @@ def get_ingredients():
         logger.error(f"Failed to fetch ingredients: {str(e)}")
         return jsonify({"error": f"Failed to fetch ingredients: {str(e)}"}), 500
 
+# Start Flask and Gradio app
 if __name__ == '__main__':
-    app.run(debug=True, host='0.0.0.0', port=7860)
+    # Launch Gradio chat interface
+    gradio_thread = demo.launch(share=True, inbrowser=True, server_port=7860, debug=True)
+
+    # Start Flask app
+    app.run(debug=True, host='0.0.0.0', port=7860)