Update app.py

app.py

@@ -4,180 +4,113 @@ from phi.agent import Agent
 from phi.model.google import Gemini
 from phi.tools.firecrawl import FirecrawlTools
 import google.generativeai as genai
-from google.generativeai import upload_file,get_file
-import io
-import base64
-
-import time
-from pathlib import Path
-import tempfile
-
-from dotenv import load_dotenv
-load_dotenv()
-
 import os
+from dotenv import load_dotenv
 
+# Load API key
+load_dotenv()
 API_KEY = os.getenv("GOOGLE_API_KEY")
 if API_KEY:
     genai.configure(api_key=API_KEY)
 
-# Page Configuration
-
+# Streamlit Page Configuration
 st.set_page_config(
     page_title="AI Shopping Partner",
     page_icon="🤖🛍️",
     layout="centered"
 )
 
-st.title("AI Shopping Partner")
-st.header("Powered by Agno and Google Gemini")
-#st.cache_resource
+# App Title & Subtitle
+st.title("🤖 AI Shopping Partner")
+st.markdown("### Find the Best Products Using AI-Powered Recommendations!")
+st.write("Upload an image, describe your preferences, and let AI find the best shopping options for you.")
 
-def get_gemini_response(api_key,prompt,image):   
-    model=genai.GenerativeModel(model_name="gemini-2.0-flash-exp")
-    response= model.generate_content([prompt,image])
+# Function to get response from Gemini AI
+def get_gemini_response(api_key, prompt, image):   
+    model = genai.GenerativeModel(model_name="gemini-2.0-flash-exp")
+    response = model.generate_content([prompt, image])
     return response.text
 
+# Function to initialize the AI Agent
 def initialize_agent():
     return Agent(
-
         name="Shopping Partner",
         model=Gemini(id="gemini-2.0-flash-exp"),  
         instructions=[
             "You are a product recommender agent specializing in finding products that match user preferences.",
-            "Prioritize finding products that satisfy as many user requirements as possible, but ensure a minimum match of 50%.",
-            "Search for products only from authentic and trusted e-commerce websites such as Google Shopping, Amazon, Flipkart, Myntra, Meesho, Nike, and other reputable platforms.",
-            "Verify that each product recommendation is in stock and available for purchase.",
-            "Avoid suggesting counterfeit or unverified products.",
-            "Clearly mention the key attributes of each product (e.g., price, brand, features) in the response.",
-            "Format the recommendations neatly and ensure clarity for ease of user understanding.",
+            "Prioritize products that match at least 50% of user requirements.",
+            "Only suggest products from trusted sources like Amazon, Flipkart, Myntra, and Nike.",
+            "Ensure the product is in stock and available for purchase.",
+            "Provide key details such as price, brand, and features in a well-structured format.",
+            "Include hyperlinks to the product whenever possible.",
         ],
         tools=[FirecrawlTools()],
         markdown=True
-        )
+    )
 
-#Initialize the Agent
+# Initialize AI Agent
 multimodal_Agent = initialize_agent()
 
-# Define acceptable file types and MIME types
-accepted_mime_types = ["image/jpeg", "image/png"]
-
-#File Uploader
-image_file = st.file_uploader("Upload a image File to Analyse and provide relevant shopping links",type=["jpg","jpeg","png"],help="Upload max 200mb image for AI Analysis")
-image= None
-
-#Prompt
-prompt= "What is in this photo?"
-
-
+# File Uploader for Image
+st.markdown("### 📤 Upload an Image")
+image_file = st.file_uploader("Choose an image file (JPG, JPEG, PNG)", type=["jpg", "jpeg", "png"])
 
-if image_file is not None:
-    # Convert the uploaded file into a BytesIO stream
-    #image_stream = io.BytesIO(image_file.read())
+if image_file:
     image = Image.open(image_file)
-    
-    try:
-        # Open the image using PIL
-        #image = Image.open(image_stream)  
-
-        # Display the image in Streamlit
-        st.image(image, caption="Uploaded Image", use_container_width=False,width=400)
-        with st.spinner("AI is processing this image and gathering insights..."):
-            response= get_gemini_response(API_KEY,prompt,image)
-            st.write(f"Product Identified using AI: {response}")
-
-    except Exception as e:
-        st.error(f"Error: Unable to open image. {e}")
-
-    # Specify the mime_type if Streamlit cannot auto-detect
-    #mime_type = image_file.type
-
-    #if mime_type:
-        #st.write(f"File MIME type detected: {mime_type}")
-        # Proceed with file processing
-    #else:
-        #st.error("Could not determine MIME type for the uploaded file. Please upload a valid file.")
-
-    #if mime_type in accepted_mime_types:
-        #st.write(f"File uploaded: {image_file.name}")
-        # Process the file as needed
-    #else:
-        #st.error(f"Unsupported file type: {mime_type}")
-
-
-
-    #prompt= st.text_input("Input prompt(e.g., 'What is in this photo?'):",key="input")
-    promptColor= st.text_input("'What Color you are looking for?'",key="inputcolor")
-    promptPurpose= st.text_input("'For what purpose you are looking for this product?'",key="inputpurpose")
-    promptBudget= st.text_input("'What is your budget?'",key="inputbudget")
-    
-    
+    st.image(image, caption="Uploaded Image", use_column_width=False, width=400)
+
+    with st.spinner("🔍 Analyzing image..."):
+        prompt = "What is in this photo?"
+        ai_response = get_gemini_response(API_KEY, prompt, image)
+        st.success(f"🎯 Product Identified: {ai_response}")
+
+    # User Preferences
+    st.markdown("### 🎨 Customize Your Preferences")
+    prompt_color = st.text_input("🔹 Preferred Color:", placeholder="E.g., Black, Red, White")
+    prompt_purpose = st.text_input("🎯 Purpose of the Product:", placeholder="E.g., Running, Casual Wear")
+    prompt_budget = st.text_input("💰 Budget Range:", placeholder="E.g., Under Rs. 5000, $100-$200")
+
+    # User Query
+    user_query = st.text_area(
+        "🔎 Additional Details",
+        placeholder="Specify any extra requirements for the product (e.g., 'Only from Nike' or 'Must be waterproof')."
+    )
 
-    user_query= st.text_area("What specific insights are you looking for from the image?",                 
-                 placeholder="Ask any questions related to the image content. The AI agent will analyze and gather more context if necessary",
-                 help="Share the specific questions or details you want to explore from the image."
-                 )
-    
-    if st.button("Search this Product",key="analyse_image_button"):
+    if st.button("🔍 Search for Products"):
         if not user_query:
-            st.warning("Please enter a query to analyse this image")
+            st.warning("⚠️ Please enter some preferences or details for the search.")
         else:
-            try:
-                with st.spinner("AI is Processing this image and gathering insights..."):
-
-                    #Upload and process the video file
-                    #processed_image = upload_file(image_path)                                      
-                    #st.write(f"processed_image: {processed_image}")
-
-                    response= get_gemini_response(API_KEY,prompt,image)
-                    #st.write(f"Product Identified: {response}")
-
-                    
-
-                    #Prompt generation for Analysis
-                    analysis_prompt =(
-                        f"""
-                        I am looking for 
-                        {response}
-                        with the below preferences: 
-                        {promptColor} 
-                        {promptPurpose}
-                        {promptBudget}
-                        Can you provide recommendations. Always make sure that you provide hyperlinks to the product.
-                        {user_query}
-                        """
-                )
-
-                    #AI Agent Processing
-                    response = multimodal_Agent.run(analysis_prompt,image=image)
-
-                    # multimodal_Agent.print_response(
-                    # "I am looking for running shoes with the following preferences: Color: Black Purpose: Comfortable for long-distance running Budget: Under Rs. 10,000. Can you provide recommendations. Also provide links to the product. Search in Myntra"
-                    # )
-
-                #Display the result
-                st.subheader("Relevant search links for the product")
-                st.markdown(response.content)
-                #st.write(response)
-
-            except Exception as error:
-                st.error(f"An error occured during analysis:{error}")
-            finally:
-                #Clean up temporary video file
-                # Path(image_path).unlink(missing_ok=True)
-                st.info("Clean up temporary image file")
-    #else:
-        #st.info("Upload a image file to start the Analysis")
-    
-        #Customize text area height
-    st.markdown(
-        """
-        <style>
-        .stTextArea textarea{
-            height:100px;   
+            with st.spinner("🔎 Searching for best matches..."):
+                analysis_prompt = f"""
+                I am looking for {ai_response} with the following preferences:
+                - Color: {prompt_color}
+                - Purpose: {prompt_purpose}
+                - Budget: {prompt_budget}
+                
+                Can you provide recommendations? Ensure the response includes product links.
+                {user_query}
+                """
+                
+                response = multimodal_Agent.run(analysis_prompt, image=image)
+
+            # Display Results
+            st.subheader("🛍️ Recommended Products & Links")
+            st.markdown(response.content)
+
+# Styling for Better UI
+st.markdown(
+    """
+    <style>
+        .stTextArea textarea {
+            height: 100px;
         }
-        </style>
-        """,
-        unsafe_allow_html=True
-
-    )
+        .stButton>button {
+            background-color: #FF4B4B;
+            color: white;
+            font-size: 16px;
+            border-radius: 10px;
+        }
+    </style>
+    """,
+    unsafe_allow_html=True
+)