Update app.py

app.py

@@ -1,64 +1,71 @@
+**`app.py`**
+```python
 import gradio as gr
-from huggingface_hub import InferenceClient
+import requests
+import os
 
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
+# Fetch your Groq API key securely from HF Secrets
+groq_api_key = os.getenv("GROQ_API_KEY")
 
+# Path to local business.txt (uploaded in your Space repo)
+business_file = os.path.join(os.path.dirname(__file__), "business.txt")
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+def chat_with_business(message, history):
+    try:
+        # Read business knowledge from local file
+        with open(business_file, "r", encoding="utf-8") as f:
+            business_info = f.read().strip()
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+        # System prompt including business details
+        system_prompt = (
+            "You are a helpful customer care assistant. "
+            "Use only the following business information to answer the user's query:\n\n" +
+            business_info +
+            "\n\nIf the answer is not in the knowledge, reply 'Yeh information abhi available nahi hai.'"
+        )
 
-    messages.append({"role": "user", "content": message})
+        # Prepare Groq API payload
+        headers = {
+            "Authorization": f"Bearer {groq_api_key}",
+            "Content-Type": "application/json"
+        }
+        payload = {
+            "model": "llama3-70b-8192",
+            "messages": [
+                {"role": "system", "content": system_prompt},
+                {"role": "user", "content": message}
+            ],
+            "temperature": 0.7
+        }
 
-    response = ""
+        # Call Groq chat endpoint
+        response = requests.post(
+            "https://api.groq.com/openai/v1/chat/completions",
+            headers=headers,
+            json=payload
+        )
+        response.raise_for_status()
+        data = response.json()
+        reply = data["choices"][0]["message"]["content"]
+        return reply
 
-    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
+    except Exception as e:
+        return f"Error: {e}"
 
-        response += token
-        yield response
+# Build Gradio interface
+with gr.Blocks(theme="soft") as demo:
+    gr.Markdown("## 🌿 My Business Bot")
+    gr.Markdown("*Ask anything about your business in Hindi-English*")
+    chatbot = gr.Chatbot(elem_id="chatbox", height=400)
+    user_input = gr.Textbox(placeholder="Type your question here...", show_label=False)
 
+    def handle_interaction(message, chat_history):
+        bot_reply = chat_with_business(message, chat_history)
+        chat_history = chat_history + [(message, bot_reply)]
+        return chat_history, ""
 
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-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)",
-        ),
-    ],
-)
-
+    user_input.submit(handle_interaction, [user_input, chatbot], [chatbot, user_input])
 
+# Launch app
 if __name__ == "__main__":
     demo.launch()