Create app.py

app.py

@@ -0,0 +1,77 @@
+import os
+from dotenv import load_dotenv
+import gradio as gr
+from langchain_google_genai.chat_models import ChatGoogleGenerativeAI
+from langchain_cohere import ChatCohere
+from langchain_core.prompts import ChatPromptTemplate
+from langchain.schema.output_parser import StrOutputParser
+
+load_dotenv()
+
+COHERE_API_KEY = os.getenv('COHERE_API_KEY')
+GOOGLE_API_KEY = os.getenv('GOOGLE_API_KEY')
+
+#! 1) LOAD THE MODEL
+def load_model(model: str, temp: float, max_tokens: int):
+    if model == "Cohere Command":
+        return ChatCohere(
+            cohere_api_key=COHERE_API_KEY,
+            max_tokens=max_tokens,
+            temperature=temp
+        )
+    else:
+        return ChatGoogleGenerativeAI(
+            model="gemini-pro",
+            google_api_key=GOOGLE_API_KEY,
+            max_tokens=max_tokens,
+            temperature=temp
+        )
+
+#! 2) CONSTRUCT THE PROMPT
+template = """
+    You are a sentiment analysis AI tool, you should accurately analyse given description and predict the underlying sentiment
+    
+    Human: Following is the description: {description}
+"""
+
+prompt_template = ChatPromptTemplate.from_template(template)
+
+def submit(model, temperature, max_tokens, input_text):
+    llm = load_model(model, temperature, max_tokens)
+    
+    #! 3) SETUP THE CHAIN
+    chain = prompt_template | llm | StrOutputParser()
+    
+    #! 4) INVOKE THE CHAIN
+    result = chain.invoke(
+        {
+            "description": input_text
+        }
+    )
+    return result
+
+def demo():
+    with gr.Blocks() as demo:
+        with gr.Tabs() as tabs:
+            
+            with gr.Tab("Model Selection"):
+                with gr.Row():
+                    model = gr.Dropdown(choices=["Cohere Command", "Gemini Pro"], label="Select Model")
+                with gr.Row():
+                    temperature = gr.Slider(minimum=0.1, maximum=1.0, value=0.7, step=0.1, label="Temperature")
+                with gr.Row():
+                    max_tokens = gr.Slider(minimum=50, maximum=2048, value=150, step=10, label="Max Tokens")
+                    
+            with gr.Tab("Sentiment Analysis"):
+                with gr.Row():
+                    input_text = gr.Textbox(lines=5, placeholder="Enter your text here...", label="Input")
+                with gr.Row():
+                    output_text = gr.Textbox(lines=5, placeholder="Output will appear here...", label="Output")
+                with gr.Row():
+                    submit_button = gr.Button("Submit")
+                    submit_button.click(submit, inputs=[model, temperature, max_tokens, input_text], outputs=output_text)
+                    
+    demo.launch(share=True)
+
+if __name__ == "__main__":
+    demo()