Create app py

app py

@@ -0,0 +1,110 @@
+import gradio as gr
+from huggingface_hub import InferenceClient
+from textblob import TextBlob
+import json
+import os
+
+client = InferenceClient("Futuresony/future_ai_12_10_2024.gguf")
+
+# Directory to store interactions and feedback
+DATA_DIR = "data"
+INTERACTIONS_FILE = os.path.join(DATA_DIR, "interactions.json")
+
+# Ensure the data directory exists
+os.makedirs(DATA_DIR, exist_ok=True)
+
+def format_alpaca_prompt(user_input, system_prompt, history):
+    """Formats input in Alpaca/LLaMA style"""
+    history_str = "\n".join([f"### Instruction:\n{h[0]}\n### Response:\n{h[1]}" for h in history])
+    prompt = f"""{system_prompt}
+{history_str}
+
+### Instruction:
+{user_input}
+
+### Response:
+"""
+    return prompt
+
+def analyze_sentiment(message):
+    """Analyze the sentiment of the user's message"""
+    blob = TextBlob(message)
+    sentiment = blob.sentiment.polarity
+    return sentiment
+
+def save_interaction(user_input, chatbot_response, feedback=None):
+    """Save the interaction and feedback to a file"""
+    interaction = {
+        "user_input": user_input,
+        "chatbot_response": chatbot_response,
+        "feedback": feedback,
+        "timestamp": "2025-02-25 04:00:30"
+    }
+    if os.path.exists(INTERACTIONS_FILE):
+        with open(INTERACTIONS_FILE, "r") as file:
+            interactions = json.load(file)
+    else:
+        interactions = []
+
+    interactions.append(interaction)
+
+    with open(INTERACTIONS_FILE, "w") as file:
+        json.dump(interactions, file, indent=4)
+
+def respond(message, history, system_message, max_tokens, temperature, top_p, feedback=None):
+    sentiment = analyze_sentiment(message)
+
+    # Adjust system message based on sentiment
+    if sentiment < -0.2:
+        system_message = "You are a sympathetic Chatbot."
+    elif sentiment > 0.2:
+        system_message = "You are an enthusiastic Chatbot."
+    else:
+        system_message = "You are a friendly Chatbot."
+
+    formatted_prompt = format_alpaca_prompt(message, system_message, history)
+
+    response = client.text_generation(
+        formatted_prompt,
+        max_new_tokens=max_tokens,
+        temperature=temperature,
+        top_p=top_p,
+    )
+
+    # ✅ Extract only the response
+    cleaned_response = response.split("### Response:")[-1].strip()
+
+    history.append((message, cleaned_response))  # ✅ Update history with the new message and response
+
+    save_interaction(message, cleaned_response, feedback)  # ✅ Save the interaction and feedback
+
+    yield cleaned_response  # ✅ Output only the answer
+
+def collect_feedback(response, feedback):
+    """Collect user feedback on the chatbot's response"""
+    save_interaction(response, feedback=feedback)
+
+feedback_interface = gr.Interface(
+    fn=collect_feedback,
+    inputs=[
+        gr.Textbox(label="Response"),
+        gr.Radio(choices=["Good", "Bad"], label="Feedback"),
+    ],
+    outputs="text",
+    title="Feedback Interface"
+)
+
+demo = gr.ChatInterface(
+    respond,
+    additional_inputs=[
+        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
+        gr.Slider(minimum=1, maximum=250, value=128, step=1, label="Max new tokens"),
+        gr.Slider(minimum=0.1, maximum=4.0, value=0.9, step=0.1, label="Temperature"),
+        gr.Slider(minimum=0.1, maximum=1.0, value=0.99, step=0.01, label="Top-p (nucleus sampling)"),
+        gr.Radio(choices=["Good", "Bad"], label="Feedback", optional=True),
+    ],
+)
+
+if __name__ == "__main__":
+    demo.launch()
+    feedback_interface.launch()