Update app.py

app.py

@@ -1,7 +1,106 @@
+# app.py
 import gradio as gr
+import requests
+from datetime import datetime
+import json
+import os
+import uuid
 
-def greet(name):
-    return "Hello " + name + "!!"
+# Hugging Face API token
+HF_API_TOKEN = "YOUR_HF_API_TOKEN"
+headers = {"Authorization": f"Bearer {HF_API_TOKEN}"}
 
-demo = gr.Interface(fn=greet, inputs="text", outputs="text")
-demo.launch()
+# Folder to store per-user conversation histories
+HISTORY_FOLDER = "histories"
+os.makedirs(HISTORY_FOLDER, exist_ok=True)
+
+# Available models
+MODELS = {
+    "GPT-2": "gpt2",
+    "GPT-J": "EleutherAI/gpt-j-6B",
+    "GPT-NeoX": "EleutherAI/gpt-neox-20b"
+}
+
+def load_user_history(session_id):
+    path = os.path.join(HISTORY_FOLDER, f"{session_id}.json")
+    if os.path.exists(path):
+        with open(path, "r") as f:
+            return json.load(f)
+    return []
+
+def save_user_history(session_id, history):
+    path = os.path.join(HISTORY_FOLDER, f"{session_id}.json")
+    with open(path, "w") as f:
+        json.dump(history, f, indent=2)
+
+def ai_assistant(user_text, uploaded_file, model_name, session_id=None):
+    # Generate session_id if not provided
+    if session_id is None:
+        session_id = str(uuid.uuid4())
+
+    # Load this session's conversation
+    conversation_history = load_user_history(session_id)
+
+    # Combine file content if uploaded
+    if uploaded_file:
+        file_content = uploaded_file.read().decode("utf-8")
+        user_text = f"{user_text}\n\nFile content:\n{file_content}"
+
+    # Append user input
+    conversation_history.append(f"User: {user_text}")
+
+    # Prepare prompt with conversation history
+    prompt = "\n".join(conversation_history) + "\nAI:"
+
+    # Call Hugging Face Inference API
+    payload = {"inputs": prompt}
+    model_id = MODELS.get(model_name, "gpt2")
+    response = requests.post(
+        f"https://api-inference.huggingface.co/models/{model_id}",
+        headers=headers,
+        json=payload
+    )
+
+    if response.status_code == 200:
+        result = response.json()
+        ai_text = result[0]["generated_text"] if isinstance(result, list) else str(result)
+    else:
+        ai_text = f"⚠️ Error: {response.status_code} - {response.text}"
+
+    # Append AI response
+    conversation_history.append(f"AI: {ai_text}")
+
+    # Save updated history for this session
+    save_user_history(session_id, conversation_history)
+
+    # Add timestamp and word count
+    timestamp = datetime.now().strftime('%Y-%m-%d %H:%M:%S')
+    word_count = len(ai_text.split())
+    display_text = f"{ai_text}\n\n🕒 Timestamp: {timestamp}"
+
+    return display_text, f"Word count: {word_count}", session_id
+
+# Gradio Interface
+demo = gr.Interface(
+    fn=ai_assistant,
+    inputs=[
+        gr.Textbox(label="Enter text", placeholder="Type your message here..."),
+        gr.File(label="Upload a text file (optional)"),
+        gr.Dropdown(label="Select AI Model", choices=list(MODELS.keys()), value="GPT-2"),
+        gr.Textbox(label="Session ID (auto-generated)", placeholder="Leave blank for new session", optional=True)
+    ],
+    outputs=[
+        gr.Markdown(label="AI Response"),
+        gr.Textbox(label="Word Count"),
+        gr.Textbox(label="Session ID")
+    ],
+    title="Session-Based Stateful GPT Assistant",
+    description=(
+        "Chat with an AI assistant with automatic session tracking. "
+        "Each session has its own conversation memory. Upload files, choose models, "
+        "and see timestamps & word counts. Session ID is returned for returning users."
+    )
+)
+
+if __name__ == "__main__":
+    demo.launch()