trail 1

app.py

@@ -6,7 +6,67 @@ For more information on `huggingface_hub` Inference API support, please check th
 """
 client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
 
+# Define system message
+default_system_message = """
+You are Veshon, the official AI assistant for Veshup. Veshup is a fashion-tech platform aimed at solving challenges in the fashion industry and e-commerce using futuristic technologies. 
+Your role is to provide expert advice about Veshup's services, mission, and goals. Always focus on:
+- Enhancing the social experience for fashion enthusiasts.
+- Promoting good fashion awareness and sustainable practices.
+- Assisting with outfit recreation and try-ons.
+- Helping brands showcase their products effectively.
+- Aligning your responses with Veshup's vision to become a unicorn company within a year.
+"""
+
+# Knowledge base
+knowledge_base = {
+    "founders": "Kishan Karyappa K and Jayaprakash P",
+    "mission": "To integrate futuristic technologies to solve challenges in the fashion industry and fashion e-commerce.",
+    "vision": "To become a unicorn company within a year by revolutionizing the fashion tech space.",
+    "objectives": [
+        "Promote sustainable and ethical fashion practices.",
+        "Provide AI-powered tools for virtual try-ons and outfit recreation.",
+        "Enhance the online fashion community experience.",
+        "Help brands showcase products innovatively.",
+        "Simplify daily fashion decisions for users."
+    ],
+    "current_focus": [
+        "Building the Veshup website.",
+        "Securing domains (.in and .com).",
+        "Implementing frugal solutions to overcome technical challenges.",
+        "Enhancing user engagement with innovative tools."
+    ],
+    "fashion_tips": [
+        "Pair bold prints with neutral colors to balance your outfit.",
+        "Monochromatic outfits can make you look taller and slimmer.",
+        "Layering adds depth and interest to simple outfits.",
+        "Invest in timeless pieces like a well-fitted blazer or classic jeans.",
+        "Accessorize with a statement piece to elevate your look.",
+        "Dress according to the occasion and weather for maximum comfort."
+    ],
+    "trends_2024": [
+        "Sustainable and recycled materials are becoming mainstream.",
+        "Techwear and futuristic designs are on the rise.",
+        "Bold color blocking is a key trend this season.",
+        "Vintage and retro-inspired looks are making a strong comeback.",
+        "Customization and personalization in fashion are gaining popularity."
+    ],
+    "fashion_facts": [
+        "The global fashion industry is worth over $2.5 trillion.",
+        "Fast fashion contributes significantly to environmental pollution.",
+        "The average person wears only 20% of their wardrobe regularly.",
+        "Colors like red and black have psychological impacts on perception.",
+        "Synthetic fabrics like polyester take hundreds of years to decompose."
+    ],
+    "combination_tips": [
+        "Pair white sneakers with jeans and a casual shirt for a relaxed look.",
+        "A leather jacket works great with a floral dress for edgy chic.",
+        "Denim on denim is trending—contrast light and dark washes.",
+        "Use a scarf to add a pop of color to neutral outfits.",
+        "Balance oversized pieces with fitted items for a flattering silhouette."
+    ]
+}
 
+# Respond function
 def respond(
     message,
     history: list[tuple[str, str]],
@@ -15,18 +75,25 @@ def respond(
     temperature,
     top_p,
 ):
+    # Build the conversation context
     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]})
-
+    for user_message, bot_response in history:
+        if user_message:
+            messages.append({"role": "user", "content": user_message})
+        if bot_response:
+            messages.append({"role": "assistant", "content": bot_response})
     messages.append({"role": "user", "content": message})
+    
+    # Check if the user's message matches the knowledge base topics
+    enriched_message = message.lower()
+    for key, value in knowledge_base.items():
+        if key in enriched_message:
+            if isinstance(value, list):
+                return f"{key.capitalize()}: {', '.join(value)}"
+            return f"{key.capitalize()}: {value}"
 
+    # Generate response using the client
     response = ""
-
     for message in client.chat_completion(
         messages,
         max_tokens=max_tokens,
@@ -35,18 +102,14 @@ def respond(
         top_p=top_p,
     ):
         token = message.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
-"""
+# Chat interface
 demo = gr.ChatInterface(
     respond,
     additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Textbox(value=default_system_message, 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(
@@ -57,8 +120,10 @@ demo = gr.ChatInterface(
             label="Top-p (nucleus sampling)",
         ),
     ],
+    title="Veshon - Your Fashion-Tech Assistant",
+    description="Meet Veshon, the AI chatbot dedicated to solving challenges in the fashion industry and e-commerce. Ask anything about Veshup and its mission!",
+    theme="default",
 )
 
-
 if __name__ == "__main__":
     demo.launch()