Update app.py

app.py

@@ -1,9 +1,10 @@
 import pandas as pd
+import gradio as gr
 from sklearn.feature_extraction.text import TfidfVectorizer
 from sklearn.metrics.pairwise import linear_kernel
-import gradio as gr
+from groq import Groq
 
-#product description for each one
+# Sample data for products
 data = {
     'product_id': [1, 2, 3, 4, 5],
     'name': ['Laptop', 'Smartphone', 'Headphones', 'Smartwatch', 'Tablet'],
@@ -16,39 +17,77 @@ data = {
     ]
 }
 
+# Initialize Groq client
+api_key = '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'
+client = Groq(api_key=api_key)
 
+# Create DataFrame
 products_df = pd.DataFrame(data)
 
-# Vectorizing the product descriptions 
+# Vectorizing the product descriptions
 tfidf = TfidfVectorizer(stop_words='english')
 tfidf_matrix = tfidf.fit_transform(products_df['description'])
 
 # Compute the cosine similarity matrix
 cosine_sim = linear_kernel(tfidf_matrix, tfidf_matrix)
 
-# Function to get product recommendations 
+# Function to get product recommendations
 def get_recommendations(product_name, cosine_sim=cosine_sim):
     idx = products_df[products_df['name'] == product_name].index[0]
     sim_scores = list(enumerate(cosine_sim[idx]))
     sim_scores = sorted(sim_scores, key=lambda x: x[1], reverse=True)
-    sim_scores = sim_scores[1:4]
+    sim_scores = sim_scores[1:4]  # Get top 3 recommendations
     product_indices = [i[0] for i in sim_scores]
-    return products_df['name'].iloc[product_indices].tolist()
-
+    return products_df.iloc[product_indices]
 
+# Function to fetch descriptions from Groq based on product names
+def fetch_descriptions(product_names):
+    descriptions = []
+    try:
+        for product_name in product_names:
+            chat_completion = client.chat.completions.create(
+                messages=[
+                    {
+                        "role": "user",
+                        "content": f"Generate description for {product_name}",
+                    }
+                ],
+                model="llama3-8b-8192",
+            )
+            description = chat_completion.choices[0].message.content.strip()
+            descriptions.append((product_name, description))
+    except Exception as e:
+        descriptions.append((product_name, f"Description fetch failed: {str(e)}"))
+    return descriptions
 
+# Function to format description in organized manner
+def format_description(product_name, description):
+    formatted_description = (
+        f"**{product_name}:**\n\n"
+        f"{description}\n\n"
+    )
+    return formatted_description
 
-#gradio
+# Function to recommend products and fetch descriptions
 def recommend_products(product_name):
-    recommendations = get_recommendations(product_name)
-    return recommendations
+    recommended_products_df = get_recommendations(product_name)
+    recommended_product_names = recommended_products_df['name'].tolist()
+    recommended_product_descriptions = fetch_descriptions(recommended_product_names)
+
+    # Combine product names and descriptions for output
+    recommendations_with_descriptions = [
+        format_description(name, desc) for name, desc in recommended_product_descriptions
+    ]
+    return '\n'.join(recommendations_with_descriptions)
+
 
+# Define Gradio interface
 iface = gr.Interface(
     fn=recommend_products,
     inputs=gr.Dropdown(choices=products_df['name'].tolist(), label="Select a product"),
-    outputs=gr.Textbox(label="Recommended Products"),
+    outputs=gr.Textbox(label="Recommended Products with Descriptions", type="text"),
     title="AI-driven E-commerce Recommendation Engine",
-    description="Select a product to get recommendations according to a similar description for another product."
+    description="Select a product to get recommendations along with their descriptions."
 )
 
 # Launch the Gradio interface