Update app.py

app.py

@@ -1,64 +1,60 @@
-import gradio as gr
-from huggingface_hub import InferenceClient
-
-"""
-For more information on `huggingface_hub` Inference API support, please check the docs: https://huggingface.co/docs/huggingface_hub/v0.22.2/en/guides/inference
-"""
-client = InferenceClient("HuggingFaceH4/zephyr-7b-beta")
-
-
-def respond(
-    message,
-    history: list[tuple[str, str]],
-    system_message,
-    max_tokens,
-    temperature,
-    top_p,
-):
-    messages = [{"role": "system", "content": system_message}]
-
-    for val in history:
-        if val[0]:
-            messages.append({"role": "user", "content": val[0]})
-        if val[1]:
-            messages.append({"role": "assistant", "content": val[1]})
-
-    messages.append({"role": "user", "content": message})
-
-    response = ""
-
-    for message in client.chat_completion(
-        messages,
-        max_tokens=max_tokens,
-        stream=True,
-        temperature=temperature,
-        top_p=top_p,
-    ):
-        token = message.choices[0].delta.content
-
-        response += token
-        yield response
-
-
-"""
-For information on how to customize the ChatInterface, peruse the gradio docs: https://www.gradio.app/docs/chatinterface
-"""
-demo = gr.ChatInterface(
-    respond,
-    additional_inputs=[
-        gr.Textbox(value="You are a friendly Chatbot.", label="System message"),
-        gr.Slider(minimum=1, maximum=2048, value=512, step=1, label="Max new tokens"),
-        gr.Slider(minimum=0.1, maximum=4.0, value=0.7, step=0.1, label="Temperature"),
-        gr.Slider(
-            minimum=0.1,
-            maximum=1.0,
-            value=0.95,
-            step=0.05,
-            label="Top-p (nucleus sampling)",
-        ),
-    ],
-)
-
+from flask import Flask, request, jsonify
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+app = Flask(__name__)
+
+# Load Hugging Face DialoGPT model
+tokenizer = AutoTokenizer.from_pretrained("microsoft/DialoGPT-medium")
+model = AutoModelForCausalLM.from_pretrained("microsoft/DialoGPT-medium")
+
+# Simulated new releases (could be replaced with TMDB API or scraped data)
+latest_releases = [
+    {
+        "title": "Neon Drift",
+        "genre": "Sci-Fi | Netflix | April 10, 2025",
+        "description": "A futuristic racer enters a deadly tournament to save his sister from a megacorp."
+    },
+    {
+        "title": "The Forgotten Bloom",
+        "genre": "Drama | Prime Video | April 9, 2025",
+        "description": "A woman unearths family secrets while restoring her grandmother’s abandoned greenhouse."
+    },
+    {
+        "title": "Shadow Protocol",
+        "genre": "Action-Thriller | Disney+ | April 12, 2025",
+        "description": "An ex-agent is forced back into action to prevent a digital war between superpowers."
+    }
+]
+
+# Helper function to generate a reply
+def generate_reply(user_input):
+    new_user_input_ids = tokenizer.encode(user_input + tokenizer.eos_token, return_tensors='pt')
+    bot_input_ids = new_user_input_ids
+    chat_history_ids = model.generate(bot_input_ids, max_length=1000, pad_token_id=tokenizer.eos_token_id)
+    reply = tokenizer.decode(chat_history_ids[:, bot_input_ids.shape[-1]:][0], skip_special_tokens=True)
+    return reply
+
+@app.route("/chat", methods=["POST"])
+def chat():
+    user_message = request.json.get("message", "").lower()
+
+    if "new" in user_message or "release" in user_message:
+        movie_list = "\n\n".join([
+            f"🎬 *{movie['title']}*\n{movie['genre']}\n_{movie['description']}_" 
+            for movie in latest_releases
+        ])
+        return jsonify({"response": f"Here are the latest releases:\n\n{movie_list}"})
+
+    elif any(title.lower() in user_message for title in [m["title"].lower() for m in latest_releases]):
+        movie = next(m for m in latest_releases if m["title"].lower() in user_message)
+        return jsonify({
+            "response": f"🎬 *{movie['title']}*\n{movie['genre']}\n\n_{movie['description']}_"
+        })
+
+    else:
+        reply = generate_reply(user_message)
+        return jsonify({"response": reply})
 
 if __name__ == "__main__":
-    demo.launch()
+    app.run(debug=True)