init

app.py

@@ -1,106 +1,53 @@
-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
 
-# 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 = []
+# Ensure that Kaggle API is authenticated correctly
+try:
+    kaggle.api.authenticate()
+except Exception as e:
+    print(f"Kaggle API authentication failed: {str(e)}")
 
-# 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
+# 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)
 
-# Gradio interface
+# Function to handle the chat interaction
 def gradio_chat(usertxt, chat_history):
-    response, updated_history = respond(usertxt, chat_history)
-    return response, updated_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)
 html_content = """
-<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>
+<h1>Food Safety Inspection Hub Prototype</h1>
+<p>Chat with our AI-powered assistant to report food safety concerns and interact with authorities.</p>
 """
 
-# Create a Gradio interface
+# Define the Gradio interface
 with gr.Blocks() as demo:
     gr.HTML(html_content)
-    chatbot = gr.ChatInterface(fn=gradio_chat)
+    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])
 
-# Launch the interface
+# Launch the Gradio interface
 demo.launch()