Create app.py

app.py

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+import gradio as gr
+import pollinations as pl
+import pandas as pd
+import os
+from datetime import datetime
+from huggingface_hub import HfApi
+import pyarrow.parquet as pq
+import pyarrow as pa
+import requests
+import json
+
+# Initialize Pollinations text model (default)
+default_model = "openai"
+model = pl.Text(model=default_model, contextual=True)
+
+# Hugging Face setup
+HF_TOKEN = os.getenv("HF_TOKEN")  # Set in HF Space secrets
+REPO_ID = "kulia-moon/conza"
+api = HfApi()
+
+# Store conversation history
+conversation_history = []
+
+# Fetch available models
+def fetch_models():
+    try:
+        response = requests.get("https://text.pollinations.ai/models")
+        response.raise_for_status()
+        models = response.json()
+        return models, gr.update(choices=[m["id"] for m in models], value=default_model)
+    except Exception as e:
+        return {"error": f"Failed to fetch models: {e}"}, gr.update(choices=[default_model], value=default_model)
+
+def change_model(selected_model):
+    global model
+    model = pl.Text(model=selected_model, contextual=True)
+    return f"Switched to model: {selected_model}"
+
+def chatbot_response(user_message, history, selected_model):
+    global conversation_history, model
+    # Ensure model is up-to-date
+    if model.model != selected_model:
+        model = pl.Text(model=selected_model, contextual=True)
+    
+    # Generate response
+    seed = int(datetime.now().timestamp())  # Use timestamp as seed
+    response = model(user_message, seed=seed)
+    
+    # Append to history with timestamp and model info
+    timestamp = datetime.now().strftime("%Y-%m-%d %H:%M:%S")
+    conversation_history.append({
+        "timestamp": timestamp,
+        "user_message": user_message,
+        "bot_response": response,
+        "model": selected_model
+    })
+    
+    # Update Gradio history
+    history.append((user_message, response))
+    
+    # Save to Parquet and push to HF
+    save_conversation()
+    
+    return history
+
+def save_conversation():
+    if not conversation_history:
+        return
+    # Convert to DataFrame
+    df = pd.DataFrame(conversation_history)
+    # Generate filename with timestamp
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    filename = f"conversation_{timestamp}.parquet"
+    # Save to Parquet
+    table = pa.Table.from_pandas(df)
+    pq.write_table(table, filename)
+    # Push to Hugging Face
+    try:
+        api.upload_file(
+            path_or_fileobj=filename,
+            path_in_repo=f"data/{filename}",
+            repo_id=REPO_ID,
+            repo_type="dataset",
+            token=HF_TOKEN
+        )
+        os.remove(filename)  # Clean up local file
+    except Exception as e:
+        print(f"Error uploading to Hugging Face: {e}")
+
+# Create Gradio interface
+with gr.Blocks() as demo:
+    gr.Markdown("# Pollinations AI Chatbot")
+    with gr.Row():
+        with gr.Column(scale=3):
+            chatbot = gr.Chatbot()
+            msg = gr.Textbox(placeholder="Type your message...")
+            model_selector = gr.Dropdown(label="Select Model", choices=[default_model])
+            change_model_btn = gr.Button("Change Model")
+            clear = gr.Button("Clear")
+        with gr.Column(scale=1):
+            models_output = gr.JSON(label="Pollinations Models")
+    
+    # Fetch models on load
+    models_output, model_selector = fetch_models()
+
+    def user_input(user_message, history):
+        return "", history + [[user_message, None]]
+
+    def bot_response(history, selected_model):
+        if not history or not history[-1][0]:
+            return history
+        user_message = history[-1][0]
+        history = chatbot_response(user_message, history, selected_model)
+        return history
+
+    # Event handlers
+    msg.submit(user_input, [msg, chatbot], [msg, chatbot], queue=False).then(
+        bot_response, [chatbot, model_selector], chatbot
+    )
+    change_model_btn.click(change_model, model_selector, gr.State())
+    clear.click(lambda: ([], []), None, [chatbot, msg], queue=False)
+
+# Launch the demo
+demo.launch()