Update app.py

app.py

@@ -4,118 +4,45 @@ 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
-import os
 
-# Load environment variables
+from dotenv import load_dotenv
 load_dotenv()
 
-# Configure Google API
+import os
+
 API_KEY = os.getenv("GOOGLE_API_KEY")
 if API_KEY:
     genai.configure(api_key=API_KEY)
 
 # Page Configuration
+
 st.set_page_config(
     page_title="AI Shopping Partner",
     page_icon="🤖🛍️",
     layout="centered"
 )
 
-# Custom CSS for styling
-st.markdown("""
-    <style>
-        /* Main container styling */
-        .main {
-            background-color: #f9fafb;
-            padding: 2rem;
-        }
-        
-        /* Title styling */
-        .title-text {
-            color: #FFFFFF;
-            font-size: 2.5rem !important;
-            font-weight: 700;
-            text-align: center;
-            margin-bottom: 1rem;
-        }
-        
-        /* Header styling */
-        .header-text {
-            color: #4a5568 !important;
-            font-size: 1.2rem !important;
-            text-align: center;
-            margin-bottom: 2rem;
-        }
-        
-        /* Upload section styling */
-        .upload-section {
-            background: white;
-            border-radius: 12px;
-            padding: 2rem;
-            box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);
-            margin-bottom: 2rem;
-        }
-        
-        /* Input field styling */
-        .stTextInput input, .stTextArea textarea {
-            border: 2px solid #e2e8f0 !important;
-            border-radius: 8px !important;
-            padding: 0.75rem !important;
-        }
-        
-        /* Button styling */
-        .stButton button {
-            background: #4299e1 !important;
-            color: white !important;
-            border: none;
-            padding: 12px 24px;
-            border-radius: 8px;
-            font-weight: 600;
-            transition: all 0.3s;
-            width: 100%;
-        }
-        
-        .stButton button:hover {
-            background: #3182ce !important;
-            transform: translateY(-1px);
-            box-shadow: 0 4px 6px -1px rgba(66, 153, 225, 0.3);
-        }
-        
-        /* Result section styling */
-        .result-section {
-            background: white;
-            border-radius: 12px;
-            padding: 2rem;
-            margin-top: 2rem;
-            box-shadow: 0 4px 6px -1px rgba(0, 0, 0, 0.1);
-        }
-        
-        /* Product card styling */
-        .product-card {
-            border-left: 4px solid #4299e1;
-            padding: 1rem;
-            margin: 1rem 0;
-            background: #f8fafc;
-            border-radius: 8px;
-        }
-    </style>
-""", unsafe_allow_html=True)
+st.title("AI Shopping Partner")
+st.header("Powered by Agno and Google Gemini")
+#st.cache_resource
 
-# Page Content
-st.markdown('<p class="title-text">🤖 AI Shopping Partner 🛍️</p>', unsafe_allow_html=True)
-st.markdown('<p class="header-text">Discover perfect products with AI-powered recommendations</p>', unsafe_allow_html=True)
+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
 
-# Initialize the Agent
 def initialize_agent():
     return Agent(
+
         name="Shopping Partner",
-        model=Gemini(id="gemini-2.0-flash-exp"),
+        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%.",
@@ -127,99 +54,130 @@ def initialize_agent():
         ],
         tools=[FirecrawlTools()],
         markdown=True
-    )
+        )
 
-# Initialize the Agent
-multimodal_agent = initialize_agent()
+#Initialize the 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?"
 
-# Function to get Gemini response
-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
 
-# Image Upload Section
-with st.container():
-    st.markdown("""
-        <div class="upload-section">
-            <h3 style="color: #2d3748; margin-bottom: 1rem;">📸 Upload Product Image</h3>
-    """, unsafe_allow_html=True)
-    
-    image_file = st.file_uploader(
-        "Drag and drop or click to upload (JPEG, PNG)",
-        type=["jpg", "jpeg", "png"],
-        help="Max file size: 20MB",
-        label_visibility="collapsed"
-    )
-    
-    st.markdown("</div>", unsafe_allow_html=True)
 
-# Display Image
 if image_file is not None:
+    # Convert the uploaded file into a BytesIO stream
+    #image_stream = io.BytesIO(image_file.read())
     image = Image.open(image_file)
-    st.image(image, caption="Uploaded Image", use_column_width=True)
-
-# Preferences Section
-with st.container():
-    st.markdown("### 🎨 Product Preferences")
-    col1, col2, col3 = st.columns(3)
-    with col1:
-        prompt_color = st.text_input("Preferred Color", placeholder="e.g., Navy Blue")
-    with col2:
-        prompt_purpose = st.text_input("Intended Use", placeholder="e.g., Running")
-    with col3:
-        prompt_budget = st.text_input("Budget Range", placeholder="e.g., ₹5,000-10,000")
-
-# Search Query Section
-user_query = st.text_area(
-    "🔍 Tell us more about what you're looking for:",
-    placeholder="Example: 'Looking for durable running shoes with good arch support'",
-    height=100
-)
+    
+    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}")
 
-# Search Button
-if st.button("🚀 Find Perfect Products"):
-    if not user_query:
-        st.warning("Please describe what you're looking for")
-    else:
-        with st.spinner("🧠 AI is analyzing your preferences..."):
+
+
+    #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")
+    
+    
+
+    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 not user_query:
+            st.warning("Please enter a query to analyse this image")
+        else:
             try:
-                # Step 1: Identify the product in the image
-                prompt = "What is in this photo?"
-                product_description = get_gemini_response(API_KEY, prompt, image)
-                
-                # Step 2: Generate analysis prompt
-                analysis_prompt = f"""
-                    I am looking for {product_description}
-                    with the below preferences:
-                    - Color: {prompt_color}
-                    - Purpose: {prompt_purpose}
-                    - Budget: {prompt_budget}
-                    Can you provide recommendations? Always make sure that you provide hyperlinks to the product.
-                    Additional details: {user_query}
-                """
-                
-                # Step 3: Get recommendations from the agent
-                response = multimodal_agent.run(analysis_prompt, image=image)
-                
-                # Display Results
-                st.markdown("""
-                    <div class="result-section">
-                        <h3 style="color: #2d3748; margin-bottom: 1rem;">🎉 Recommended Products</h3>
-                """, unsafe_allow_html=True)
-                
-                # Display the response content
-                st.markdown(response.content, unsafe_allow_html=True)
-                
-                st.markdown("</div>", unsafe_allow_html=True)
-                
+                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 occurred: {str(error)}")
-
-# Footer
-st.markdown("""
-    <div style="text-align: center; color: #718096; margin-top: 3rem;">
-        <hr style="margin-bottom: 1rem;">
-        <p>Powered by Gemini AI • Secure Processing • Always Learning</p>
-    </div>
-""", unsafe_allow_html=True)
+                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;   
+        }
+        </style>
+        """,
+        unsafe_allow_html=True
+
+    )