Create app.py

app.py

@@ -0,0 +1,47 @@
+import gradio as gr
+import openai
+import json
+from pinecone import Pinecone
+pc = Pinecone(api_key='72f7d24d-c080-4ce6-a060-a489713284cb')
+index_name = 'imdb-vector'
+# connect to index
+index = pc.Index(index_name)
+# get api key from platform.openai.com
+openai.api_key = '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'
+embed_model = "text-embedding-ada-002"
+from openai import OpenAI
+client = OpenAI(api_key=openai.api_key)
+def generate_context(text):
+    body=json.dumps({"inputText": text})
+    res = client.embeddings.create(
+        input=[text],
+        model=embed_model
+    ).data[0].embedding
+    result = index.query(vector=res, top_k=7, include_metadata=True)
+    contexts = []
+    contexts = contexts + [x['metadata']['text'] for x in result['matches']]
+    return contexts
+    
+def invoke_openai(prompt):
+    sys_prompt = "You are a helpful assistant that always answers questions."
+    # query text-davinci-003
+    res = client.chat.completions.create(
+        model='gpt-3.5-turbo',
+        messages=[
+            {"role": "system", "content": sys_prompt},
+            {"role": "user", "content": prompt}
+        ],
+        temperature=0
+    )
+    return res.choices[0].message.content
+    
+def build_prompt(message,history):
+    context=generate_context(message)
+    messages=[]
+    prompt=f'Context - {context}\nBased on the above context, answer this question - {message}'
+    print(prompt)
+    return invoke_openai(prompt) 
+     
+
+iface = gr.ChatInterface(build_prompt, chatbot=gr.Chatbot(height=300), textbox=gr.Textbox(placeholder="Ask me a question", container=False, scale=7), title="IMDB Sentiment Data Analysis Chat", examples = ["Which is most postive review for a movie?", "What is the most reviewed movie?"], theme="soft", cache_examples=False, retry_btn=None, undo_btn="Delete Previous", clear_btn="Clear",)
+iface.launch(share=True