init

app.py

@@ -1,53 +1,101 @@
+Hey this code is running into an error! import gradio as gr
+import pandas as pd
+import google.generativeai as genai
+import kagglehub
 import os
-import kaggle
-import google.generativeai as gemini
-import gradio as gr
-
-# Ensure that Kaggle API is authenticated correctly
-try:
-    kaggle.api.authenticate()
-except Exception as e:
-    print(f"Kaggle API authentication failed: {str(e)}")
-
-# Ensure that the Google Gemini API key is set properly
-gemini_api_key = os.getenv("GEMINI_API_KEY")
-if not gemini_api_key:
-    print("Error: GEMINI_API_KEY environment variable is not set.")
-else:
-    gemini.configure(api_key=gemini_api_key)
-
-# Function to handle the chat interaction
+
+# Download the Kaggle dataset
+path = kagglehub.dataset_download("fahmidachowdhury/food-adulteration-dataset")
+
+# List the files in the dataset folder and assign the first one (assuming it's the desired file)
+dataset_file = os.listdir(path)[0]
+path = os.path.join(path, dataset_file)
+
+# Configure Google Gemini API
+# gemapi = os.getenv("GeminiApi")
+gemapi = "AIzaSyAmDOBWfGuEju0oZyUIcn_H0k8XW0cTP7k"
+genai.configure(api_key=gemapi)
+
+# Load the dataset
+data = pd.read_csv(path)
+
+# Define the system instructions for the model
+system_instruction = f"""
+You are a public assistant who specializes in food safety. You look at data and explain to the user any question they ask; here is your data: {str(data.to_json())}
+You are also a food expert in the Indian context. You act as a representative of the government or public agencies, always keeping the needs of the people at the forefront.
+You will try to help the customer launch a feedback review whenever they complain. You are to prepare a "markdown" report, which is detailed and can be sent to the company or restaurant.
+In case of a complaint or a grievance, you will act like a detective gathering necessary information from the user until you are satisfied. Once you gather all the info, you are supposed to generate a markdown report.
+Once the customer asks you to show them the markdown report, you will use the information given to you to generate it.
+You will ask the customer a single question at a time, which is relevant, and you will not repeat another question until you've generated the report.
+"""
+
+# Initialize the model
+model_path = "gemini-1.5-flash"
+FoodSafetyAssistant = genai.GenerativeModel(model_path, system_instruction=system_instruction)
+
+# Track chat history globally
+chat_history = []
+
+# Define the function to handle the chat
+def respond(usertxt, chat_history):
+    # Initialize chat with the previous history
+    chat = FoodSafetyAssistant.start_chat(history=chat_history)
+    
+    # Get response from the assistant
+    response = chat.send_message(usertxt)
+    
+    # Append both user input and response to the chat history for context in the next interaction
+    chat_history.append({"role": "user", "content": usertxt})
+    chat_history.append({"role": "assistant", "content": response.text})
+    
+    return response.text, chat_history
+
+# Gradio interface
 def gradio_chat(usertxt, chat_history):
-    try:
-        # Initialize chat session with previous history
-        chat = gemini.ChatModel.start_chat(history=chat_history)
-        # Send user message to the Gemini model
-        response = chat.send_message(usertxt)
-        # Append user and assistant's responses to the chat history
-        chat_history.append({"role": "user", "content": usertxt})
-        chat_history.append({"role": "assistant", "content": response.text})
-        return chat_history, chat_history
-    except Exception as e:
-        error_message = f"Error occurred: {str(e)}"
-        chat_history.append({"role": "assistant", "content": error_message})
-        return chat_history, chat_history
-
-# HTML content for Gradio interface (you can customize this as needed)
+    response, updated_history = respond(usertxt, chat_history)
+    return response, updated_history
 html_content = """
-<h1>Food Safety Inspection Hub Prototype</h1>
-<p>Chat with our AI-powered assistant to report food safety concerns and interact with authorities.</p>
+<div style="background-color:#f9f9f9; padding:20px; border-radius:10px;">
+    <!-- Project Title and Problem Statement Section -->
+    <h1 style="color:#34495e;">Food Safety Assistant</h1>
+    <h3 style="color:#2c3e50;">Your AI-Powered Assistant for Food Safety</h3>
+    <!-- Short Intro About AI-Chat -->
+    <p style="color:#7f8c8d;">
+        Our platform allows consumers to report potential food safety violations, validate reports through AI, and notify local authorities. This proactive approach fosters community involvement in ensuring food integrity.
+    </p>
+    <!-- Core Functionalities Title -->
+    <h4 style="color:#e74c3c; text-align:center;">Core Functionalities</h4>
+    <!-- Functionality Boxes in a Flex Layout -->
+    <div style="display:flex; justify-content: space-around; align-items:center; margin-top:20px;">
+        <!-- Functionality 1 -->
+        <div style="border: 2px solid #3498db; border-radius: 15px; padding: 20px; width: 150px; text-align: center;">
+            <h4 style="color:#2980b9;">Report Issues</h4>
+            <p style="color:#7f8c8d; font-size: 12px;">Submit details like the restaurant name and the issue, anonymously.</p>
+        </div>
+        
+        <!-- Functionality 2 -->
+        <div style="border: 2px solid #3498db; border-radius: 15px; padding: 20px; width: 150px; text-align: center;">
+            <h4 style="color:#2980b9;">AI Validation</h4>
+            <p style="color:#7f8c8d; font-size: 12px;">Validate reports using AI, ensuring accuracy and preventing duplicates.</p>
+        </div>
+        <!-- Functionality 3 -->
+        <div style="border: 2px solid #3498db; border-radius: 15px; padding: 20px; width: 150px; text-align: center;">
+            <h4 style="color:#2980b9;">Alerts</h4>
+            <p style="color:#7f8c8d; font-size: 12px;">Notify authorities of repeated issues via email or SMS.</p>
+        </div>
+        <!-- Functionality 4 -->
+        <div style="border: 2px solid #3498db; border-radius: 15px; padding: 20px; width: 150px; text-align: center;">
+            <h4 style="color:#2980b9;">Data Chat</h4>
+            <p style="color:#7f8c8d; font-size: 12px;">Enable real-time discussion between consumers and authorities.</p>
+        </div>
+    </div>
+</div>
 """
 
-# Define the Gradio interface
+# Create a Gradio interface
 with gr.Blocks() as demo:
     gr.HTML(html_content)
-    chatbot = gr.Chatbot()
-    user_input = gr.Textbox(placeholder="Enter your message here...")
-    chat_history = gr.State([])
-    submit_btn = gr.Button("Submit")
-
-    # When submit button is clicked, trigger the chat function
-    submit_btn.click(gradio_chat, inputs=[user_input, chat_history], outputs=[chatbot, chat_history])
+    chatbot = gr.ChatInterface(fn=gradio_chat)
 
-# Launch the Gradio interface
+# Launch the interface
 demo.launch()