Upload app.py

app.py

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+# Step 1: Import Libraries
+import os
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import gradio as gr
+
+# Step 2: Load Environment Variables
+# Load token from environment variable
+HUGGING_FACE_TOKEN = os.getenv("HF_TOKEN")
+
+# Step 3: Load the Model with Authentication
+# Load Mistral 7B with authentication
+mistral_tokenizer = AutoTokenizer.from_pretrained(
+    "mistralai/Mistral-7B-v0.1",
+    use_auth_token=HUGGING_FACE_TOKEN
+)
+mistral_model = AutoModelForCausalLM.from_pretrained(
+    "mistralai/Mistral-7B-v0.1",
+    use_auth_token=HUGGING_FACE_TOKEN,
+    torch_dtype=torch.float16,  # Use half-precision for faster inference
+    device_map="auto"  # Automatically loads the model on GPU if available
+)
+
+# Step 4: Define the Chatbot Logic
+def generate_response(prompt):
+    # Tokenize the input prompt
+    inputs = mistral_tokenizer(prompt, return_tensors="pt").to(mistral_model.device)
+    
+    # Generate the response
+    outputs = mistral_model.generate(**inputs, max_length=200)
+    
+    # Decode the response
+    response = mistral_tokenizer.decode(outputs[0], skip_special_tokens=True)
+    return response
+
+# Step 5: Create the Gradio Interface
+def chatbot(prompt):
+    response = generate_response(prompt)
+    return response
+
+interface = gr.Interface(
+    fn=chatbot,
+    inputs="text",
+    outputs="text",
+    title="Mistral 7B Chatbot",
+    description="Ask questions and get answers from Mistral 7B!"
+)
+
+# Step 6: Launch the App
+interface.launch()