Update app.py

app.py

@@ -1,50 +1,77 @@
 import os
 import json
 import gradio as gr
+from transformers import pipeline
 
-# Load Dataset Function
+# Step 1: Load JSON Dataset
 def load_dataset(folder_path):
-    """Loads all JSON files from a folder and subfolders into a single dataset."""
     data = []
-    for root, dirs, files in os.walk(folder_path):
-        for file_name in files:
-            if file_name.endswith(".json"):
-                file_path = os.path.join(root, file_name)
-                try:
-                    with open(file_path, "r") as f:
-                        file_data = json.load(f)
-                        data.append(file_data)
-                except Exception as e:
-                    print(f"Error loading {file_name}: {e}")
-    if not data:
-        print("No valid JSON files found in the dataset folder.")
-        return [{"question": "No dataset available.", "answer": "Please upload a valid dataset."}]
+    for filename in os.listdir(folder_path):
+        if filename.endswith(".json"):
+            with open(os.path.join(folder_path, filename), "r") as file:
+                data.extend(json.load(file))  # Assuming each file is a list of entries
     return data
 
+# Step 2: Initialize OpenAI or Hugging Face Model
+model = pipeline("question-answering", model="deepset/roberta-base-squad2")  # Replace with your preferred model
 
-# Initialize Dataset
-DATASET_PATH = os.path.join(os.path.dirname(__file__), "dataset")
-dataset = load_dataset(DATASET_PATH)
-
-# Chatbot Response Function
-def chatbot_response(user_input):
-    """Generate a response based on user input and the dataset."""
+# Step 3: Query Handler
+def query_chatbot(query, name, email, contact):
+    # Load the dataset
+    dataset = load_dataset("dataset/")
+    
+    # Retrieve relevant information using a simple RAG technique
+    responses = []
     for entry in dataset:
-        if "question" in entry and "answer" in entry:
-            if user_input.lower() in entry["question"].lower():
-                return entry["answer"]
-    return "I'm sorry, I couldn't find relevant information. Please provide more details."
-
-# Gradio Interface
-interface = gr.Interface(
-    fn=chatbot_response,
-    inputs="text",
-    outputs="text",
-    title="Education Consultant Chatbot",
-    description="Ask questions about study programs, visas, and more!",
-    theme="compact"
-)
-
-# Run App
+        context = entry.get("content", "")  # Extract relevant content from the JSON file
+        if query.lower() in context.lower():
+            response = model(question=query, context=context)
+            responses.append(response["answer"])
+
+    # Compile the result
+    response = (
+        f"Hello {name}!\n\n"
+        f"Based on your query: '{query}', here are some relevant insights:\n\n"
+        + "\n".join(responses)[:3]  # Limit to top 3 responses
+    )
+    
+    # Create a profile (Optional enhancement)
+    profile = {
+        "name": name,
+        "email": email,
+        "contact": contact,
+        "query": query,
+        "responses": responses,
+    }
+
+    # Optionally, you can send this profile to an email or save it for further analysis.
+    return response
+
+# Step 4: Gradio Interface
+def chatbot_ui():
+    with gr.Blocks() as app:
+        gr.Markdown("# 🎓 Education Consultant Chatbot")
+        
+        with gr.Row():
+            with gr.Column():
+                query_input = gr.Textbox(label="Your Query", placeholder="Ask about courses, visas, or programs...")
+                name_input = gr.Textbox(label="Name", placeholder="Your Full Name")
+                email_input = gr.Textbox(label="Email", placeholder="Your Email Address")
+                contact_input = gr.Textbox(label="Contact (Optional)", placeholder="Your Contact Number")
+                submit_btn = gr.Button("Submit")
+            
+            with gr.Column():
+                output_text = gr.Textbox(label="Chatbot Response")
+        
+        submit_btn.click(
+            query_chatbot,
+            inputs=[query_input, name_input, email_input, contact_input],
+            outputs=output_text,
+        )
+
+    return app
+
+# Step 5: Launch App
 if __name__ == "__main__":
-    interface.launch()
+    chatbot = chatbot_ui()
+    chatbot.launch()