Update app.py

app.py

@@ -8,15 +8,26 @@ import tempfile
 from transformers import pipeline
 
 
-# Load Two Models
-light_chat_model = pipeline(
-    "text-generation",
-    model="mistralai/Mistral-7B-Instruct-v0.3",
-    revision="refs/pr/23",  # Use latest stable revision if needed
-    use_auth_token="HUGGINGFACE_TOKEN",
-    max_length=256
-)
-advanced_summarizer = pipeline("summarization", model="facebook/bart-large-cnn")
+# Your Hugging Face API Token (set this safely)
+HF_TOKEN = os.getenv("HUGGINGFACE_TOKEN")  # Recommended: use environment variable
+
+# Mistral Inference API URL
+API_URL = "https://api-inference.huggingface.co/models/mistralai/Mistral-7B-Instruct-v0.3"
+
+# Headers for authentication
+headers = {
+    "Authorization": f"Bearer {HF_TOKEN}"
+}
+
+# Function to query Mistral API
+def query_mistral(question):
+    payload = {
+        "inputs": question,
+        "parameters": {"max_length": 256}
+    }
+    response = requests.post(API_URL, headers=headers, json=payload)
+    output = response.json()
+    return output[0]["generated_text"]
 
 def answer_investing_question(question):
     response = light_chat_model(question)[0]['generated_text']
@@ -202,12 +213,12 @@ with gr.Blocks(theme="soft") as iface:
             output_sector = gr.Dataframe()
         with gr.Tab("Historical Price Chart"):
             output_chart = gr.Plot()
-        with gr.Blocks(theme="soft") as iface:
+       with gr.Blocks(theme="soft") as iface:
             with gr.Tab("Ask About Investing"):
                 user_question = gr.Textbox(label="Ask about investing...")
                 answer_box = gr.Textbox(label="Answer")
                 ask_button = gr.Button("Get Answer")
-                ask_button.click(fn=answer_investing_question, inputs=[user_question], outputs=[answer_box])
+                ask_button.click(fn=query_mistral, inputs=[user_question], outputs=[answer_box])
 
 
     submit_btn = gr.Button("Run Analysis")