Update app.py

app.py

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+import torch
+import os
+import gradio as gr
+
+#from langchain.llms import OpenAI
+from langchain.llms import HuggingFaceHub
+
+from transformers import pipeline
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+
+from ibm_watson_machine_learning.foundation_models import Model
+from ibm_watson_machine_learning.foundation_models.extensions.langchain import WatsonxLLM
+from ibm_watson_machine_learning.metanames import GenTextParamsMetaNames as GenParams
+
+my_credentials = {
+    "url"    : "https://us-south.ml.cloud.ibm.com"
+}
+params = {
+        GenParams.MAX_NEW_TOKENS: 800, # The maximum number of tokens that the model can generate in a single run.
+        GenParams.TEMPERATURE: 0.1,   # A parameter that controls the randomness of the token generation. A lower value makes the generation more deterministic, while a higher value introduces more randomness.
+    }
+
+LLAMA2_model = Model(
+        model_id= 'meta-llama/llama-2-70b-chat', 
+        credentials=my_credentials,
+        params=params,
+        project_id="skills-network",  
+        )
+
+llm = WatsonxLLM(LLAMA2_model)  
+
+#######------------- Prompt Template-------------####
+
+temp = """
+<s><<SYS>>
+List the key points with details from the context: 
+[INST] The context : {context} [/INST] 
+<</SYS>>
+"""
+
+pt = PromptTemplate(
+    input_variables=["context"],
+    template= temp)
+
+prompt_to_LLAMA2 = LLMChain(llm=llm, prompt=pt)
+
+#######------------- Speech2text-------------####
+
+def transcript_audio(audio_file):
+    # Initialize the speech recognition pipeline
+    pipe = pipeline(
+        "automatic-speech-recognition",
+        model="openai/whisper-tiny.en",
+        chunk_length_s=30,
+    )
+    # Transcribe the audio file and return the result
+    transcript_txt = pipe(audio_file, batch_size=8)["text"]
+    result = prompt_to_LLAMA2.run(transcript_txt)
+
+    return result
+
+#######------------- Gradio-------------####
+
+audio_input = gr.Audio(sources="upload", type="filepath")
+output_text = gr.Textbox()
+
+iface = gr.Interface(fn= transcript_audio, 
+                    inputs= audio_input, outputs= output_text, 
+                    title= "Audio Transcription App",
+                    description= "Upload the audio file")
+
+iface.launch(server_name="0.0.0.0", server_port=7860)