Delete app.py

app.py

@@ -1,180 +0,0 @@
-import openai
-from pinecone import Pinecone, ServerlessSpec
-import pandas as pd
-import gradio as gr
-from typing import List, Tuple
-
-# Function to get embeddings from OpenAI's model
-def get_embedding(text: str, openai_api_key: str, model: str = "text-embedding-ada-002") -> List[float]:
-    openai.api_key = openai_api_key
-    try:
-        response = openai.Embedding.create(
-            model=model,
-            input=text
-        )
-        return response['data'][0]['embedding']
-    except Exception as e:
-        print(f"Error getting embedding: {e}")
-        return []
-
-# Function to process the uploaded CSV and store embeddings in Pinecone
-def process_csv(file, openai_api_key: str, pinecone_api_key: str, pinecone_env: str) -> str:
-    try:
-        df = pd.read_csv(file.name)
-        
-        # Initialize Pinecone
-        pc = Pinecone(api_key=pinecone_api_key)
-        index_name = "product-recommendations"
-        
-        # Check if index exists
-        if index_name not in pc.list_indexes().names():
-            try:
-                pc.create_index(
-                    name=index_name,
-                    dimension=1536,
-                    spec=ServerlessSpec(cloud="aws", region=pinecone_env)
-                )
-            except Exception as e:
-                print(f"Error creating Pinecone index: {e}")
-                return "Failed to create Pinecone index."
-
-        index = pc.Index(index_name)
-        
-        embeddings = []
-        for i, row in df.iterrows():
-            embedding = get_embedding(row['description'], openai_api_key)
-            if embedding:
-                embeddings.append((str(row['product_id']), embedding, {'product_name': row['product_name'], 'image_url': row['image_url']}))
-        
-        if embeddings:
-            try:
-                index.upsert(embeddings)
-            except Exception as e:
-                print(f"Error upserting embeddings to Pinecone: {e}")
-                return "Failed to upsert embeddings."
-        
-        return "Product catalog processed and embeddings stored in Pinecone."
-    except Exception as e:
-        print(f"Error processing CSV file: {e}")
-        return "Failed to process CSV file."
-
-# Recommendation logic
-def recommend_products(query: str, openai_api_key: str, pinecone_api_key: str, pinecone_env: str, top_k: int = 10) -> List[Tuple[str, str]]:
-    query_embedding = get_embedding(query, openai_api_key)
-    
-    if not query_embedding:
-        return []
-
-    try:
-        # Initialize Pinecone
-        pc = Pinecone(api_key=pinecone_api_key)
-        index = pc.Index("product-recommendations")
-        
-        results = index.query(vector=query_embedding, top_k=top_k, include_metadata=True)
-        recommended_products = [(match['metadata']['image_url'], f"{match['metadata']['product_name']} (Score: {match['score']})") for match in results['matches']]
-        return recommended_products
-    except Exception as e:
-        print(f"Error querying Pinecone: {e}")
-        return []
-
-# Function to generate contextual message
-def generate_contextual_message(query: str, recommendations: List[Tuple[str, str]], openai_api_key: str, system_prompt: str) -> str:
-    openai.api_key = openai_api_key
-    product_names = [rec[1] for rec in recommendations]
-    prompt = f"User query: {query}\nRecommended products: {', '.join(product_names)}\n{system_prompt}"
-    
-    try:
-        response = openai.ChatCompletion.create(
-            model="gpt-4",  # or use "gpt-3.5-turbo" if preferred
-            messages=[{"role": "system", "content": "You are a helpful assistant."},
-                      {"role": "user", "content": prompt}]
-        )
-        return response['choices'][0]['message']['content']
-    except Exception as e:
-        print(f"Error generating contextual message: {e}")
-        return "Failed to generate contextual message."
-
-# Gradio interface
-def handle_file_upload(file, openai_api_key, pinecone_api_key, pinecone_env):
-    return process_csv(file, openai_api_key, pinecone_api_key, pinecone_env)
-
-def display_recommendations(user_input, openai_api_key, pinecone_api_key, pinecone_env, system_prompt):
-    recommendations = recommend_products(user_input, openai_api_key, pinecone_api_key, pinecone_env)
-    contextual_message = generate_contextual_message(user_input, recommendations, openai_api_key, system_prompt)
-    return recommendations, contextual_message
-
-# Function to update outputs
-def update_outputs(query_input, openai_api_key, pinecone_api_key, pinecone_env, chat_history, system_prompt):
-    recommendations, contextual_message = display_recommendations(query_input, openai_api_key, pinecone_api_key, pinecone_env, system_prompt)
-    
-    # Update chat history
-    new_chat_history = chat_history + [[query_input, contextual_message]]
-    
-    return recommendations, new_chat_history, gr.update(value="")
-
-css = """
-.lg.svelte-cmf5ev {background-color: #8A2BE2 !important;}
-.user.svelte-1pjfiar.svelte-1pjfiar.svelte-1pjfiar {padding: 7px !important;border-radius: 10px 10px 0px 10px;width: fit-content;background-color: #E6E6FA !important ;border-color:#E6E6FA !important}
-.bot.svelte-1pjfiar.svelte-1pjfiar.svelte-1pjfiar {padding: 7px !important;border-radius: 10px 10px 10px 0px;width : fit-content !important;border: 1.5px solid #9370DB !important;background: #FFFFFF 0% 0% no-repeat padding-box !important;box-shadow: 0px 3px 6px #0000001A !important;border: 2px solid #9370DB !important;}
-.primary.svelte-cmf5ev {box-shadow: 0px 3px 6px #0000001A !important;border: 2px solid #9370DB !important;background: #8A2BE2 !important;width: fit-content;}
-.primary.svelte-cmf5ev {color: white !important}
-textarea.scroll-hide.svelte-1f354aw {font-family:'Roboto','Arial',sans-serif;font-size:14px}
-label.svelte-1b6s6s { background: #9370DB 0% 0% no-repeat padding-box;color: white;width: 100%;}
-label.svelte-1b6s6s {background: #9370DB 0% 0% no-repeat padding-box;color: white;width: 100%;font-size:20px;font-family:'Roboto','Arial',sans-serif; border-radius: 0px 0px 10px 10px;}
-.wrapper.svelte-nab2ao{background-color : #F7F7F7 }
-svg.iconify.iconify--carbon{width:15px; height:15px}
-.thumbnail-item.svelte-fiatpe.svelte-fiatpe:hover {--ring-color: #9370DB !important;}
-"""
-
-default_chat = [["Welcome! I'm your AI-powered product recommendation bot. Ask me anything about finding the perfect product for you.", "I'm here to assist you with any product-related inquiries. Let's find what you need!"]]
-
-# Create Gradio Interface
-def build_interface():
-    with gr.Blocks(title="AI Smart Shopper", head="True", css=css) as interface:
-        gr.Markdown("""<div style="text-align: center; font-weight: bold;"> <h1>AI Smart Shopper</h1> </div>""")
-        
-        with gr.Tab("API Keys"):
-            openai_api_key_input = gr.Textbox(label="OpenAI API Key", type="password")
-            pinecone_api_key_input = gr.Textbox(label="Pinecone API Key", type="password")
-            pinecone_env_input = gr.Textbox(label="Pinecone Environment", placeholder="e.g., us-east-1")
-            system_prompt_input = gr.Textbox(label="System Prompt", placeholder="Enter a system prompt for the assistant...")
-
-        with gr.Tab("Upload Catalog"):
-            upload_button = gr.File(label="Upload CSV", type="filepath")
-            output = gr.Textbox()
-            upload_button.upload(handle_file_upload, inputs=[upload_button, openai_api_key_input, pinecone_api_key_input, pinecone_env_input], outputs=output)
-        
-        with gr.Tab("Get Recommendations"):
-            with gr.Row():
-                with gr.Column(scale=1):
-                    chatbot = gr.Chatbot(value=default_chat, label="Recommender Chatbot", show_label=True)
-                    query_input = gr.Textbox(label="Enter your product preference...", show_label=False, placeholder="Type your query here...")
-                    with gr.Row():
-                        with gr.Column(scale=1, min_width=150):
-                            recommend_button = gr.Button("Get Recommendations")
-                        with gr.Column(scale=1, min_width=150):
-                            clear_button = gr.Button("Clear")
-                    # Define state for chat history
-                    chat_history = gr.State([])
-
-                    # Define outputs
-                with gr.Column(scale=1):
-                    recommendations_output = gr.Gallery(label="Recommendations For You", show_label=False, elem_id="gallery", columns=[3], rows=[1], object_fit="contain", height="auto", scale=5)
-
-                recommend_button.click(
-                    update_outputs,
-                    inputs=[query_input, openai_api_key_input, pinecone_api_key_input, pinecone_env_input, chat_history, system_prompt_input],
-                    outputs=[recommendations_output, chatbot, query_input]
-                )
-
-                clear_button.click(
-                    lambda: (gr.update(value=default_chat), gr.update(value=""), gr.update(value=[]), gr.update(value=[])),
-                    outputs=[chatbot, query_input, chat_history, recommendations_output]
-                )
-                
-    return interface
-
-# Run the interface
-if __name__ == "__main__":
-    interface = build_interface()
-    interface.launch()