Create app.py

app.py

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+import streamlit as st
+import requests
+
+# Step 1: Exchange API Key for Access Token
+apikey = "aSSAcZP0GaAaMVb_3VMB7_69eG5bEP_MzmIQBm_TrwmG"  # Replace with your new IBM Cloud API key
+auth_url = "https://us-south.ml.cloud.ibm.com"
+
+auth_headers = {
+    "Content-Type": "application/x-www-form-urlencoded",
+    "Accept": "application/json"
+}
+
+auth_data = {
+    "grant_type": "urn:ibm:params:oauth:grant-type:apikey",
+    "apikey": apikey
+}
+
+auth_response = requests.post(auth_url, headers=auth_headers, data=auth_data)
+
+if auth_response.status_code == 200:
+    access_token = auth_response.json()["access_token"]
+else:
+    st.error(f"Failed to retrieve access token: {auth_response.text}")
+    st.stop()
+
+# Title of the Streamlit app
+st.title("IBM Watson Text Generation with Hugging Face Integration")
+
+# User input for the text generation prompt
+user_input = st.text_area("Enter your prompt:", value="You are Granite Chat, an AI language model developed by IBM. You are a cautious assistant.")
+
+# IBM Watson API request parameters
+url = "https://us-south.ml.cloud.ibm.com/ml/v1/text/generation?version=2023-05-29"
+body = {
+    "input": f"""<|system|>{user_input}<|assistant|>""",
+    "parameters": {
+        "decoding_method": "sample",
+        "max_new_tokens": 900,
+        "temperature": 0.7,
+        "top_k": 50,
+        "top_p": 1,
+        "repetition_penalty": 1.05
+    },
+    "model_id": "ibm-granite/granite-34b-code-instruct",
+    "project_id": "73c61b5e-83ae-4158-8ad6-8d3c8089146a"
+}
+
+headers = {
+    "Accept": "application/json",
+    "Content-Type": "application/json",
+    "Authorization": f"Bearer {access_token}"
+}
+
+# Step 2: Generate text using IBM Watson when the button is clicked
+if st.button("Generate Text"):
+    response = requests.post(url, headers=headers, json=body)
+
+    if response.status_code == 200:
+        data = response.json()
+
+        # Accessing the 'generated_text' from the first result in 'results'
+        if 'results' in data and len(data['results']) > 0:
+            generated_text = data['results'][0]['generated_text']
+            st.write("**IBM Watson Output:**")
+            st.write(generated_text)
+        else:
+            st.error("No results found in the response.")
+    else:
+        st.error(f"Error {response.status_code}: {response.text}")