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.env

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+{ "GOOGLE_API_KEY": "AIzaSyAHd1zIOKcXM8xJPt4T-F55jN00u_4Phlw" }

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+test_image.png filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,13 +1 @@
----
-title: Kashif
-emoji: 🐢
-colorFrom: red
-colorTo: indigo
-sdk: streamlit
-sdk_version: 1.32.2
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# code-prep-gemini-ai-course

config.json

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+{ "GOOGLE_API_KEY": "AIzaSyAHd1zIOKcXM8xJPt4T-F55jN00u_4Phlw" }

gemini_utility.py

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+import os
+import json
+from PIL import Image
+
+import google.generativeai as genai
+
+# working directory path
+working_dir = os.path.dirname(os.path.abspath(__file__))
+
+# path of config_data file
+config_file_path = f"{working_dir}/config.json"
+config_data = json.load(open("config.json"))
+
+# loading the GOOGLE_API_KEY
+GOOGLE_API_KEY = config_data["GOOGLE_API_KEY"]
+
+# configuring google.generativeai with API key
+genai.configure(api_key=GOOGLE_API_KEY)
+
+
+def load_gemini_pro_model():
+    gemini_pro_model = genai.GenerativeModel("gemini-pro")
+    return gemini_pro_model
+
+
+# get response from Gemini-Pro-Vision model - image/text to text
+def gemini_pro_vision_response(prompt, image):
+    gemini_pro_vision_model = genai.GenerativeModel("gemini-pro-vision")
+    response = gemini_pro_vision_model.generate_content([prompt, image])
+    result = response.text
+    return result
+
+
+# get response from embeddings model - text to embeddings
+def embeddings_model_response(input_text):
+    embedding_model = "models/embedding-001"
+    embedding = genai.embed_content(model=embedding_model,
+                                    content=input_text,
+                                    task_type="retrieval_document")
+    embedding_list = embedding["embedding"]
+    return embedding_list
+
+
+# get response from Gemini-Pro model - text to text
+def gemini_pro_response(user_prompt):
+    gemini_pro_model = genai.GenerativeModel("gemini-pro")
+    response = gemini_pro_model.generate_content(user_prompt)
+    result = response.text
+    return result
+
+
+# result = gemini_pro_response("What is Machine Learning")
+# print(result)
+# print("-"*50)
+#
+#
+# image = Image.open("test_image.png")
+# result = gemini_pro_vision_response("Write a short caption for this image", image)
+# print(result)
+# print("-"*50)
+#
+#
+# result = embeddings_model_response("Machine Learning is a subset of Artificial Intelligence")
+# print(result)

main.py

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+import os
+
+from PIL import Image
+import streamlit as st
+from streamlit_option_menu import option_menu
+
+from gemini_utility import (load_gemini_pro_model,
+                            gemini_pro_response,
+                            gemini_pro_vision_response,
+                            embeddings_model_response)
+
+
+working_dir = os.path.dirname(os.path.abspath(__file__))
+
+st.set_page_config(
+    page_title="Gemini AI",
+    page_icon="🧠",
+    layout="centered",
+)
+
+with st.sidebar:
+    selected = option_menu('Gemini AI',
+                           ['Marketing Campaign',
+                            'ChatBot',
+                            'Image Captioning',
+                            'Embed text',
+                            'Ask me anything'],
+                           menu_icon='robot', icons=['chat-dots-fill', 'image-fill', 'textarea-t', 'patch-question-fill'],
+                           default_index=0
+                           )
+
+
+# Function to translate roles between Gemini-Pro and Streamlit terminology
+def translate_role_for_streamlit(user_role):
+    if user_role == "model":
+        return "assistant"
+    else:
+        return user_role
+
+
+
+# Marketing Campaign section
+if selected == "Marketing Campaign":
+    st.title("📢 Marketing Campaign")
+
+    #product_name = st.text_input("Product Name", " ")
+    product_name = st.text_input(
+        "What is the name of the product? \n\n", key="product_name", value=" "
+    )
+    product_category = st.radio(
+        "Select your product category: \n\n",
+        ["Clothing", "Electronics", "Food", "Health & Beauty", "Home & Garden"],
+        key="product_category",
+        horizontal=True,
+    )
+    target_audience = st.selectbox("Target Audience", ["Teens", "Adults", "Seniors"])
+    st.write("Select your target audience: ")
+    target_audience_age = st.radio(
+        "Target age: \n\n",
+        ["18-24", "25-34", "35-44", "45-54", "55-64", "65+"],
+        key="target_audience_age",
+        horizontal=True,
+    )
+    #target_audience_gender = st.radio("Target gender: \n\n",["male","female","trans","non-binary","others"],key="target_audience_gender",horizontal=True)
+    target_audience_location = st.radio(
+        "Target location: \n\n",
+        ["Urban", "Suburban", "Rural"],
+        key="target_audience_location",
+        horizontal=True,
+    )
+    campaign_objective = st.multiselect("Campaign Objective", ["Awareness", "Engagement", "Conversion"], default=["Awareness"])
+    #budget = st.slider("Budget", 1000, 10000, step=500, value=5000)
+    
+    #--------------------------------------------------------------
+    st.write("Select your marketing campaign goal: ")
+    campaign_goal = st.multiselect(
+        "Select your marketing campaign goal: \n\n",
+        [
+            "Increase brand awareness",
+            "Generate leads",
+            "Drive sales",
+            "Improve brand sentiment",
+        ],
+        key="campaign_goal",
+        default=["Increase brand awareness", "Generate leads"],
+    )
+    if campaign_goal is None:
+        campaign_goal = ["Increase brand awareness", "Generate leads"]
+    brand_voice = st.radio(
+        "Select your brand voice: \n\n",
+        ["Formal", "Informal", "Serious", "Humorous"],
+        key="brand_voice",
+        horizontal=True,
+    )
+    estimated_budget = st.radio(
+        "Select your estimated budget ($): \n\n",
+        ["1,000-5,000", "5,000-10,000", "10,000-20,000", "20,000+"],
+        key="estimated_budget",
+        horizontal=True,
+    )
+
+    prompt = f"""Generate a marketing campaign for {product_name}, a {product_category} designed for the age group: {target_audience_age}.
+    The target location is this: {target_audience_location}.
+    Aim to primarily achieve {campaign_goal}.
+    Emphasize the product's unique selling proposition while using a {brand_voice} tone of voice.
+    Allocate the total budget of {estimated_budget}.
+    With these inputs, make sure to follow following guidelines and generate the marketing campaign with proper headlines: \n
+    - Briefly describe company, its values, mission, and target audience.
+    - Highlight any relevant brand guidelines or messaging frameworks.
+    - Provide a concise overview of the campaign's objectives and goals.
+    - Briefly explain the product or service being promoted.
+    - Define your ideal customer with clear demographics, psychographics, and behavioral insights.
+    - Understand their needs, wants, motivations, and pain points.
+    - Clearly articulate the desired outcomes for the campaign.
+    - Use SMART goals (Specific, Measurable, Achievable, Relevant, and Time-bound) for clarity.
+    - Define key performance indicators (KPIs) to track progress and success.
+    - Specify the primary and secondary goals of the campaign.
+    - Examples include brand awareness, lead generation, sales growth, or website traffic.
+    - Clearly define what differentiates your product or service from competitors.
+    - Emphasize the value proposition and unique benefits offered to the target audience.
+    - Define the desired tone and personality of the campaign messaging.
+    - Identify the specific channels you will use to reach your target audience.
+    - Clearly state the desired action you want the audience to take.
+    - Make it specific, compelling, and easy to understand.
+    - Identify and analyze your key competitors in the market.
+    - Understand their strengths and weaknesses, target audience, and marketing strategies.
+    - Develop a differentiation strategy to stand out from the competition.
+    - Define how you will track the success of the campaign.
+   -  Utilize relevant KPIs to measure performance and return on investment (ROI).
+   Give proper bullet points and headlines for the marketing campaign. Do not produce any empty lines.
+   Be very succinct and to the point.
+    """
+    #----------------------------------------------------------
+
+    if st.button("Generate Campaign"):
+        # Assuming you have a function to generate marketing campaigns
+        campaign_details = gemini_pro_response(product_name + " " + target_audience + " " + " ".join(campaign_objective) + " " + str( ))
+        st.markdown(campaign_details)
+
+
+
+# chatbot page
+if selected == 'ChatBot':
+    model = load_gemini_pro_model()
+
+    # Initialize chat session in Streamlit if not already present
+    if "chat_session" not in st.session_state:  # Renamed for clarity
+        st.session_state.chat_session = model.start_chat(history=[])
+
+    # Display the chatbot's title on the page
+    st.title("🤖 ChatBot")
+
+    # Display the chat history
+    for message in st.session_state.chat_session.history:
+        with st.chat_message(translate_role_for_streamlit(message.role)):
+            st.markdown(message.parts[0].text)
+
+    # Input field for user's message
+    user_prompt = st.chat_input("Ask Gemini-Pro...")  # Renamed for clarity
+    if user_prompt:
+        # Add user's message to chat and display it
+        st.chat_message("user").markdown(user_prompt)
+
+        # Send user's message to Gemini-Pro and get the response
+        gemini_response = st.session_state.chat_session.send_message(user_prompt)  # Renamed for clarity
+
+        # Display Gemini-Pro's response
+        with st.chat_message("assistant"):
+            st.markdown(gemini_response.text)
+
+
+# Image captioning page
+if selected == "Image Captioning":
+
+    st.title("📷 Snap Narrate")
+
+    uploaded_image = st.file_uploader("Upload an image...", type=["jpg", "jpeg", "png"])
+
+    if st.button("Generate Caption"):
+        image = Image.open(uploaded_image)
+
+        col1, col2 = st.columns(2)
+
+        with col1:
+            resized_img = image.resize((800, 500))
+            st.image(resized_img)
+
+        default_prompt = "write a short caption for this image"  # change this prompt as per your requirement
+
+        # get the caption of the image from the gemini-pro-vision LLM
+        caption = gemini_pro_vision_response(default_prompt, image)
+
+        with col2:
+            st.info(caption)
+
+
+# text embedding model
+if selected == "Embed text":
+
+    st.title("🔡 Embed Text")
+
+    # text box to enter prompt
+    user_prompt = st.text_area(label='', placeholder="Enter the text to get embeddings")
+
+    if st.button("Get Response"):
+        response = embeddings_model_response(user_prompt)
+        st.markdown(response)
+
+
+# text embedding model
+if selected == "Ask me anything":
+
+    st.title("❓ Ask me a question")
+
+    # text box to enter prompt
+    user_prompt = st.text_area(label='', placeholder="Ask me anything...")
+
+    if st.button("Get Response"):
+        response = gemini_pro_response(user_prompt)
+        st.markdown(response)

requirements.txt

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+google-generativeai==0.4.0
+pillow==10.2.0
+streamlit==1.31.1
+streamlit-option-menu==0.3.12