Update app.py

app.py

@@ -2,75 +2,71 @@ import gradio as gr
 import pandas as pd
 from huggingface_hub import InferenceClient
 
- # Load AI model for reasoning
-# You can swap to another LLM available on Hugging Face
+# Load AI model for reasoning
 client = InferenceClient("microsoft/phi-4")
 
- # Load clothing database CSV
-# CSV must have: name, image_path, category, formality, weather, notes
+# Load clothing database CSV
 clothing_df = pd.read_csv("clothes.csv")
 
- #Simple filtering function
+# --- Simple filtering function ---
 def get_suggestions(query):
     results = clothing_df
-
-     # Weather-based filtering
-    if "rain" in query.lower():
-        results = results[results['weather'].str.contains("rain", case=False)]
-    elif "cold" in query.lower():
-        results = results[results['weather'].str.contains("cold", case=False)]
-    elif "hot" in query.lower():
-        results = results[results['weather'].str.contains("hot", case=False)]
-    elif "snow" in query.lower():
-        results = results[results['weather'].str.contains("snow", case=False)]
-
-     # Formality-based filtering
-    elif "formal" in query.lower() or "office" in query.lower():
-        results = results[results['formality'].str.contains("formal", case=False)]
-    elif "casual" in query.lower():
-        results = results[results['formality'].str.contains("casual", case=False)]
-
-     # If no keywords match, return random sample
+    
+    q = query.lower()
+    # Weather-based filtering
+    if "rain" in q:
+        results = results[results['weather'].str.contains("rain", case=False, na=False)]
+    elif "cold" in q:
+        results = results[results['weather'].str.contains("cold", case=False, na=False)]
+    elif "hot" in q:
+        results = results[results['weather'].str.contains("hot", case=False, na=False)]
+    elif "snow" in q:
+        results = results[results['weather'].str.contains("snow", case=False, na=False)]
+
+    # Formality-based filtering
+    elif "formal" in q or "office" in q:
+        results = results[results['formality'].str.contains("formal", case=False, na=False)]
+    elif "casual" in q:
+        results = results[results['formality'].str.contains("casual", case=False, na=False)]
+
+    # If no keywords match, return random sample
+    if len(results) == 0:
+        results = clothing_df.sample(min(3, len(clothing_df)))
     else:
-        results = results.sample(min(4, len(results)))
-
-
+        results = results.sample(min(3, len(results)))
 
     return results
 
- # Chatbot + image output 
+# --- Chatbot + image output ---
 def respond(message, history):
-    # Send to LLM for reasoning
+    # Build conversation messages for AI model
     messages = [{"role": "system", "content": "You are a clothing assistant. Suggest suitable clothing items from the database based on the user's needs."}]
-    if history:
-        for user_msg, bot_msg in history:
-            messages.append({"role": "user", "content": user_msg})
-            messages.append({"role": "assistant", "content": bot_msg})
+    for user_msg, bot_msg in history:
+        messages.append({"role": "user", "content": user_msg})
+        messages.append({"role": "assistant", "content": bot_msg})
     messages.append({"role": "user", "content": message})
-    
-    ai_response = client.chat_completion(messages, max_tokens=150)
-    reasoning = ai_response['choices'][0]['message']['content'].strip()
 
-     # Get filtered clothing
-    matches = get_suggestions(message)
+    # Query Hugging Face Inference API
+    ai_response = client.chat_completion(model="microsoft/phi-4", messages=messages, max_tokens=150)
+    reasoning = ai_response.choices[0].message["content"].strip()
 
-     # Create image list for gallery
+    # Get filtered clothing
+    matches = get_suggestions(message)
     image_paths = matches['image_path'].tolist()
 
-    return reasoning, image_paths
+    # Add to chatbot history
+    history.append((message, reasoning))
+    return history, image_paths
 
- # --- Gradio UI ---
+# --- Gradio UI ---
 with gr.Blocks() as demo:
-    chatbot = gr.Chatbot(label="Chatbot Conversation", type="messages")
-    gallery = gr.Gallery(label="Recommended Clothing", height="auto")
-    btn = gr.Button("Show Images")
+    gr.Markdown("## Fashioneer - your fashion pioneer!")
+    gr.Markdown("Ask me what to wear and I'll suggest clothing with images from the database.")
+
+    chatbot = gr.Chatbot(label="Chatbot Conversation")
+    gallery = gr.Gallery(label="Recommended Clothing", columns=3, height="auto")
+    user_input = gr.Textbox(label="Type your outfit requirements here", placeholder="e.g., What should I wear on a rainy day?")
 
-    def show_images():
-        return[
-            ("https://picsum.photos/200", "Image 1"),
-            ("https://picsum.photos/201", "Image 2"),
-            ("https://picsum.photos/202", "Image 3")
-        ]
-    btn.click(fn=show_images, outputs=gallery)
+    user_input.submit(respond, [user_input, chatbot], [chatbot, gallery])
 
-demo.launch()
+demo.launch()