Merge branch 'master'

.gitignore

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+# .gitignore
+
+# Python
+__pycache__/
+
+# Virtual Environment
+venv/
+gem/
+
+# IDE files
+.idea/
+.vscode/
+
+# Compiled Python files
+*.pyc
+*.pyo
+*.pyd
+
+# Python cache
+*.pyc
+
+# Jupyter Notebook
+.ipynb_checkpoints/

Gemini.py

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+import streamlit as st
+import pandas as pd
+
+# Data provided
+data = {
+    'Capability': ['MMLU', 'CoT@32*', 'Big-Bench Hard', 'DROP', 'HellaSwag', 'GSM8K', 'maj1@32', 'MATH', 'HumanEval', 'Natural2Code'],
+    'Benchmark': ['Representation of questions in 57 subjects', 'Reasoning', 'Reading comprehension (F1 Score)', 'Commonsense reasoning for everyday tasks', 'Basic arithmetic manipulations', 'Basic arithmetic manipulations', 'Python code generation', 'Challenging math problems', 'Python code generation', 'Python code generation'],
+    'Higher is better': [90.0, 86.4, 83.6, 82.4, 87.8, 94.4, 92.0, 53.2, 74.4, 74.9],
+    'Description': ['CoT@32*', '5-shot** (reported)', '3-shot', 'Variable shots', '10-shot*', 'maj1@32', '5-shot CoT (reported)', '4-shot', '0-shot (IT)*', '0-shot']
+}
+# Create a DataFrame from the data
+df = pd.DataFrame(data)
+
+# Page Title
+st.title("Google's Gemini Model Guide 🚀🤖")
+
+# Introduction
+st.header("Introduction to Google's Gemini Model")
+st.sidebar.markdown("[Why Gemini?](#why-gemini)")
+
+st.write("""
+Welcome to the forefront of cutting-edge AI with Google's Gemini model! 🚀🤖 
+In this comprehensive guide, we'll dive deep into the intricacies of leveraging the power of GenerativeAI through Gemini. From seamless installation steps to fine-tuning configurations, and exploring the myriad benefits, this guide is your key to unlocking the full potential of this groundbreaking model.
+""")
+
+# Why Gemini?
+st.header("🌐 Why Gemini?")
+st.sidebar.markdown("[Installation](#installation)")
+
+st.write("""
+Gemini stands at the intersection of innovation and AI prowess. Its capabilities extend beyond the ordinary, promising a new era of generative content creation that's both powerful and dynamic.
+""")
+st.table(df)
+# Learn More section
+st.header("📚 Learn More")
+# Add a clickable link
+st.markdown("""
+For a deeper understanding and additional insights, check out accompanying [blog post](https://deepmind.google/technologies/gemini/#introduction) dedicated to Google's Gemini Model. Let's embark on this exciting journey together! 🌟🔍.
+""", unsafe_allow_html=True)

pages/1 Gemini Pro-text.py

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+import google.generativeai as genai
+import streamlit as st
+
+# Function to initialize session state
+def initialize_session_state():
+    return st.session_state.setdefault('api_key', None)
+
+# Main Streamlit app
+def text_page():
+    st.title("Gemini NexusCraft")
+
+    # Initialize session state
+    initialize_session_state()
+
+    # Configure API key
+    api_key = st.sidebar.text_input("Enter your API key:", value=st.session_state.api_key)
+
+    # Check if the API key is provided
+    if not api_key:
+        st.sidebar.error("Please enter your API key.")
+        st.stop()
+    else:
+        # Store the API key in session state
+        st.session_state.api_key = api_key
+
+    genai.configure(api_key=api_key)
+
+
+    # Set up the model configuration options
+    temperature = st.sidebar.slider("Temperature", 0.0, 1.0, 0.9, 0.1)
+    top_p = st.sidebar.number_input("Top P", 0.0, 1.0, 1.0, 0.1)
+    top_k = st.sidebar.number_input("Top K", 1, 100, 1)
+    max_output_tokens = st.sidebar.number_input("Max Output Tokens", 1, 5000, 2048)
+
+    # Set up the model
+    generation_config = {
+        "temperature": temperature,
+        "top_p": top_p,
+        "top_k": top_k,
+        "max_output_tokens": max_output_tokens,
+    }
+
+
+    safety_settings = [
+      {
+        "category": "HARM_CATEGORY_HARASSMENT",
+        "threshold": "BLOCK_MEDIUM_AND_ABOVE"
+      },
+      {
+        "category": "HARM_CATEGORY_HATE_SPEECH",
+        "threshold": "BLOCK_MEDIUM_AND_ABOVE"
+      },
+      {
+        "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
+        "threshold": "BLOCK_MEDIUM_AND_ABOVE"
+      },
+      {
+        "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
+        "threshold": "BLOCK_MEDIUM_AND_ABOVE"
+      }
+    ]
+    prompt = st.text_input("Enter your Query:")
+    # Check if the query is provided
+    if not prompt:
+        st.error("Please enter your query.")
+        st.stop()
+
+    if st.button("Ask Gemini"):
+        gemini = genai.GenerativeModel(model_name="gemini-pro",
+                                      generation_config=generation_config,
+                                      safety_settings=safety_settings)
+
+
+        prompt_parts = [prompt]
+
+        response = gemini.generate_content(prompt_parts)
+        # st.write(response.text)
+        st.write(response.text)
+
+# Run the Streamlit app
+if __name__ == "__main__":
+    text_page()

pages/2 Gemini Pro Vision.py

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+import google.generativeai as genai
+import streamlit as st
+import json
+import requests
+import re
+
+# Function to initialize session state
+def initialize_session_state():
+    return st.session_state.setdefault('api_key', None)
+
+# Main Streamlit app
+def home():
+    st.title("Gemini NexusCraft")
+
+    # Initialize session state
+    initialize_session_state()
+
+    # Configure API key
+    api_key = st.sidebar.text_input("Enter your API key:", value=st.session_state.api_key)
+
+    # Check if the API key is provided
+    if not api_key:
+        st.sidebar.error("Please enter your API key.")
+        st.stop()
+    else:
+        # Store the API key in session state
+        st.session_state.api_key = api_key
+
+    # Check if the API key is provided
+    if not api_key:
+        st.sidebar.error("Please enter your API key.")
+        st.stop()
+    genai.configure(api_key=api_key)
+
+
+    model = 'gemini-pro-vision' 
+    # Sidebar option to choose image source
+    image_source = st.sidebar.radio("Choose Image Source:", ["URL", "Upload"])
+    prompt = st.text_input("Enter your Query:")
+    if image_source == "URL":
+        # Input for image URL
+        image_url = st.sidebar.text_input("Enter the image URL:")
+        # Prepare content for Gemini model
+        contents = [
+                {
+                    "parts": [
+                        {
+                            "text":prompt
+                        },
+                        {
+                            "image": {
+                                "image_url": image_url
+                            }
+                        }
+                    ]
+                }
+            ]
+
+    else:
+        # Input for uploading an image
+        image_url = st.sidebar.file_uploader("Upload Image", type=["jpg", "jpeg", "png"])
+
+
+        # Prepare content for Gemini model
+        contents = [
+                {
+                    "parts": [
+                        {
+                            "text":prompt
+                        },
+                        {
+                            "image": {
+                                "uploaded_file": image_url
+                            }
+                        }
+                    ]
+                }
+            ]
+
+        # Set up the model configuration options
+    temperature = st.sidebar.slider("Temperature", 0.0, 1.0, 0.9, 0.1)
+    top_p = st.sidebar.number_input("Top P", 0.0, 1.0, 1.0, 0.1)
+    top_k = st.sidebar.number_input("Top K", 1, 100, 1)
+    max_output_tokens = st.sidebar.number_input("Max Output Tokens", 1, 5000, 2048)
+
+    # Set up the model
+    generation_config = {
+        "temperature": temperature,
+        "top_p": top_p,
+        "top_k": top_k,
+        "max_output_tokens": max_output_tokens,
+    }
+
+    safety_settings = [
+        {
+            "category": "HARM_CATEGORY_HARASSMENT",
+            "threshold": "BLOCK_MEDIUM_AND_ABOVE"
+        },
+        {
+            "category": "HARM_CATEGORY_HATE_SPEECH",
+            "threshold": "BLOCK_MEDIUM_AND_ABOVE"
+        },
+        {
+            "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
+            "threshold": "BLOCK_MEDIUM_AND_ABOVE"
+        },
+        {
+            "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
+            "threshold": "BLOCK_MEDIUM_AND_ABOVE"
+        }
+        ]
+    # Check if the query is provided
+    if not prompt:
+        st.error("Please enter your query.")
+        st.stop()
+
+    # Generate content and display the response
+    if st.button("Ask Gemini"):
+
+        # generation_config = json.loads(generation_config)
+        # safety_settings = json.loads(safety_settings)
+        stream = False
+        for content in contents:
+            for n, part in enumerate(content['parts']):
+                if image:=part.get('image', None):
+                    if uploaded_file := image.get('uploaded_file', None):
+                        data = uploaded_file.read()
+                        mime_type = uploaded_file.type
+                    elif image_url:=image.get('image_url', None):
+                        response = requests.get(image_url)
+                        data = response.content
+                        mime_type = response.headers['content-type']
+                    else:
+                        raise ValueError('Either uploaded_file or image_url must be provided.')
+
+                    if mime_type is None:
+                        # Guess!
+                        mime_type = 'image/png'
+
+                    blob = {'data': data, 'mime_type': mime_type}
+                    content['parts'][n] = blob
+        st.image(data)
+        gemini = genai.GenerativeModel(model_name=model)
+
+        response = gemini.generate_content(
+            contents,
+            generation_config=generation_config,
+            safety_settings=safety_settings,
+            stream=False)
+        st.subheader("Gemini Model Response:")
+        # st.write(response.candidates)
+        # st.write(response.candidates[0])
+        data_str = str(response.candidates[0])
+
+        # Use regular expressions to extract the text part
+        match = re.search(r'text: "(.*?)"', data_str)
+        if match:
+            extracted_text = match.group(1)
+            st.write(extracted_text)
+
+# Run the Streamlit app
+if __name__ == "__main__":
+    home()

readme.md

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+# Gemini NexusCraft
+
+This project consists of two Streamlit applications that leverage the capabilities of the Gemini AI model provided by Google's GenerativeAI. The Gemini model is a powerful text and image generation model that can be used for various creative and informative purposes.
+
+## Table of Contents
+- [Installation](#installation)
+- [Usage](#usage)
+  - [Text Generation](#text-generation)
+  - [Text and Image Generation](#text-and-image-generation)
+- [Configuration](#configuration)
+- [Contributing](#contributing)
+- [License](#license)
+
+## Installation
+
+1. Clone the repository:
+
+```bash
+git clone https://github.com/Devparihar5/Gemini-NexusCraft.git
+cd Gemini-NexusCraft
+```
+
+2. Install the required dependencies:
+
+```bash
+pip install -r requirements.txt
+```
+
+## Usage
+
+### Text Generation
+
+To run the Streamlit app for text generation, execute the following command:
+
+```bash
+streamlit run Gemini.py
+```
+
+This app allows you to interact with the Gemini model by providing a text prompt. It also provides configuration options such as temperature, top_p, top_k, and max_output_tokens.
+
+### Text and Image Generation
+
+
+## Configuration
+
+In both apps, you need to enter your API key to authenticate with the Gemini model. The API key can be entered through the Streamlit sidebar. Make sure to enter a valid API key to access the model.
+
+Additionally, you can configure various model parameters such as temperature, top_p, top_k, and max_output_tokens to control the generation process.
+
+## Contributing
+
+Contributions to this project are welcome. If you have any ideas, improvements, or bug fixes, feel free to open an issue or submit a pull request.
+
+## License
+
+This project is licensed under the [MIT License](LICENSE). Feel free to use and modify the code for your own purposes.

requirements.txt

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+google-generativeai==0.3.1
+pandas==2.1.4
+streamlit==1.29.0
+