Update app.py

app.py

@@ -1,64 +1,95 @@
 import gradio as gr
+import pandas as pd
+import re
 from huggingface_hub import InferenceClient
 
-"""
-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
-"""
+# Load inventory dataset
+df = pd.read_csv("inventory.csv")
+
+# Hugging Face LLM client
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
+# 🧞 RetailGenie logic
+def retailgenie_query(user_input: str) -> str:
+    user_input = user_input.lower()
 
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
+    if "where can i find" in user_input:
+        product = user_input.split("find")[-1].strip()
+        match = df[df['Product Name'].str.lower().str.contains(product)]
+        if not match.empty:
+            return f"{product.title()} is in {match.iloc[0]['Aisle']}."
+        else:
+            return "Sorry, I couldn't find that product."
+
+    elif "in stock" in user_input:
+        product = re.sub(r"(is|in stock|\?)", "", user_input).strip()
+        match = df[df['Product Name'].str.lower().str.contains(product)]
+        if not match.empty:
+            return f"{product.title()} is {'available' if match.iloc[0]['In Stock'].lower() == 'yes' else 'out of stock'}."
+        else:
+            return "Product not found."
+
+    elif "suggest" in user_input and "under" in user_input:
+        category = user_input.split("suggest a")[-1].split("under")[0].strip()
+        try:
+            price = int(re.findall(r"\d+", user_input)[-1])
+        except:
+            return "Please enter a valid price."
+        matches = df[(df['Category'].str.lower().str.contains(category)) & (df['Price (rupees)'] <= price)]
+        if not matches.empty:
+            return "Here are some options: " + ", ".join(matches['Product Name'].values[:3])
+        else:
+            return "No items found in that range."
+
+    elif "deal" in user_input or "deals" in user_input:
+        electronics = df[df['Category'].str.lower() == "electronics"]
+        deals = electronics[electronics['Price (rupees)'] < 5000]
+        if not deals.empty:
+            return "Deals: " + ", ".join(deals['Product Name'].values[:3])
+        else:
+            return "No current deals found in electronics."
+
+    return None  # Not a RetailGenie-style query
 
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
+# 🔁 Unified response function
+def respond(message, history: list[tuple[str, str]], system_message, max_tokens, temperature, top_p):
+    # First try product-aware logic
+    rg_reply = retailgenie_query(message)
+    if rg_reply:
+        yield rg_reply
+        return
 
+    # Else use LLM
+    messages = [{"role": "system", "content": system_message}]
+    for user, assistant in history:
+        if user: messages.append({"role": "user", "content": user})
+        if assistant: messages.append({"role": "assistant", "content": assistant})
     messages.append({"role": "user", "content": message})
 
     response = ""
-
-    for message in client.chat_completion(
+    for msg in client.chat_completion(
         messages,
         max_tokens=max_tokens,
         stream=True,
         temperature=temperature,
         top_p=top_p,
     ):
-        token = message.choices[0].delta.content
-
+        token = msg.choices[0].delta.content
         response += token
         yield response
 
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
+# 🧠 Gradio Chat UI
 demo = gr.ChatInterface(
     respond,
+    title="🧞 RetailGenie",
+    description="Ask me where to find products, check availability, get price-based suggestions, or talk to an AI.",
     additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Textbox(value="You are a helpful retail assistant.", 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)",
-        ),
-    ],
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.95, step=0.05, label="Top-p (nucleus sampling)"),
+    ]
 )
 
-
 if __name__ == "__main__":
     demo.launch()