Create app.py

app.py

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+import streamlit as st
+import json
+from ibm_watson import DiscoveryV2
+from ibm_cloud_sdk_core.authenticators import IAMAuthenticator
+from ibm_watson_machine_learning.foundation_models import Model
+from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames as GenParams
+from ibm_watson_machine_learning.foundation_models.utils.enums import DecodingMethods
+
+# Configuration: API Keys and URLs
+DISCOVERY_API_KEY = '5sSmoI6y0ZHP7D3a6Iu80neypsbK3tsUZR_VdRAb7ed2'
+WATSONX_API_KEY = 'zf-5qgRvW-_RMBGb0bQw5JPPGGj5wdYpLVypdjQxBGJz'
+WATSONX_PROJECT_ID = '32a4b026-a46a-48df-aae3-31e16caabc3b'
+DISCOVERY_SERVICE_URL = 'https://api.us-south.discovery.watson.cloud.ibm.com/instances/62dc0387-6c6f-4128-b479-00cf5dea09ef'
+
+# Watsonx Model Setup
+url = "https://us-south.ml.cloud.ibm.com"
+DEFAULT_MODEL = "meta-llama/llama-3-1-70b-instruct"
+max_tokens = 100
+min_tokens = 20
+decoding = DecodingMethods.GREEDY
+temperature = 0.7
+
+# IBM Watson Discovery Setup
+discovery_authenticator = IAMAuthenticator(DISCOVERY_API_KEY)
+discovery = DiscoveryV2(
+    version='2020-08-30',
+    authenticator=discovery_authenticator
+)
+discovery.set_service_url(DISCOVERY_SERVICE_URL)
+
+# Define the model generator function
+def get_model():
+    generate_params = {
+        GenParams.MAX_NEW_TOKENS: max_tokens,
+        GenParams.MIN_NEW_TOKENS: min_tokens,
+        GenParams.DECODING_METHOD: decoding,
+        GenParams.TEMPERATURE: temperature,
+    }
+    model = Model(
+        model_id=DEFAULT_MODEL,
+        params=generate_params,
+        credentials={"apikey": WATSONX_API_KEY, "url": url},
+        project_id=WATSONX_PROJECT_ID
+    )
+    return model
+
+# Function to query Watson Discovery and Watsonx Model
+def get_answer(question):
+    try:
+        response = discovery.query(
+            project_id='016da9fc-26f5-464a-a0b8-c9b0b9da83c7',
+            collection_ids=['1d91d603-cd71-5cf5-0000-019325bcd328'],
+            passages={'enabled': True, 'max_per_document': 5, 'find_answers': True},
+            natural_language_query=question
+        ).get_result()
+
+        passages = response['results'][0].get('document_passages', [])
+        context = '\n'.join([p['passage_text'] for p in passages]) or "No relevant information found."
+
+        prompt = (
+            "<s>[INST] <<SYS>> "
+            "Answer the question briefly. If you can't, say 'unanswerable'. "
+            "Question: " + question + '<</SYS>>' + context + '[/INST]'
+        )
+
+        model = get_model()
+        generated_response = model.generate(prompt)
+        return generated_response['results'][0]['generated_text']
+    except Exception as e:
+        return f"Error: {str(e)}"
+
+# Streamlit UI setup
+st.set_page_config(page_title="Advanced AI Chat", layout="wide")
+st.markdown("""
+    <style>
+    .chat-container {
+        max-width: 800px;
+        margin: 0 auto;
+        padding: 20px;
+    }
+    .chat-bubble {
+        padding: 10px 15px;
+        border-radius: 10px;
+        margin-bottom: 10px;
+        font-size: 16px;
+        line-height: 1.5;
+    }
+    .user-question {
+        background-color: #007bff;
+        color: white;
+        align-self: flex-end;
+    }
+    .ai-response {
+        background-color: #f1f1f1;
+        color: black;
+    }
+    .input-container {
+        display: flex;
+        justify-content: center;
+        position: fixed;
+        bottom: 10px;
+        width: 100%;
+    }
+    .input-box {
+        width: 70%;
+        padding: 10px;
+        border-radius: 20px;
+        border: 1px solid #ddd;
+        outline: none;
+        font-size: 16px;
+    }
+    .send-button {
+        padding: 10px 20px;
+        background-color: #007bff;
+        color: white;
+        border: none;
+        border-radius: 20px;
+        cursor: pointer;
+        font-size: 16px;
+        margin-left: 10px;
+    }
+    .send-button:hover {
+        background-color: #0056b3;
+    }
+    </style>
+""", unsafe_allow_html=True)
+
+st.markdown('<div class="chat-container">', unsafe_allow_html=True)
+
+if 'history' not in st.session_state:
+    st.session_state['history'] = []
+
+question = st.text_input("", placeholder="Type your question here...", key="input")
+
+if st.button("Send"):
+    if question.strip():
+        response = get_answer(question.strip())
+        st.session_state['history'].append({"question": question, "answer": response})
+
+for entry in st.session_state['history']:
+    st.markdown(f'<div class="chat-bubble user-question">{entry["question"]}</div>', unsafe_allow_html=True)
+    st.markdown(f'<div class="chat-bubble ai-response">{entry["answer"]}</div>', unsafe_allow_html=True)
+
+st.markdown('</div>', unsafe_allow_html=True)